dissertation topics on robotics

Research Topics & Ideas: Robotics

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring robotics and/or automation-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of research ideas , including real-world examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Robotics & Automation Research Ideas

• Developing AI algorithms for autonomous decision-making in self-driving cars.
• The impact of robotic automation on employment in the manufacturing sector.
• Investigating the use of drone technology for agricultural crop monitoring and management.
• The role of robotics in enhancing surgical precision in minimally invasive procedures.
• Analyzing the ethical implications of using robots in elderly care.
• The effectiveness of humanoid robots in assisting children with autism.
• Investigating the integration of IoT and robotics in smart home systems.
• The impact of automation on workflow efficiency in the healthcare industry.
• Analyzing the challenges of human-robot interaction in industrial settings.
• The role of robotics in deep-sea exploration and data collection.
• Investigating the use of robotic exoskeletons in rehabilitation therapy for stroke patients.
• The impact of artificial intelligence on the future of job skills and training.
• Developing advanced machine learning models for robotic vision and object recognition.
• Analyzing the role of robots in disaster response and search-and-rescue missions.
• The effectiveness of collaborative robots (cobots) in small-scale industries.
• Investigating the potential of robotics in renewable energy operations and maintenance.
• The role of automation in enhancing precision agriculture techniques.
• Analyzing the security risks associated with industrial automation systems.
• The impact of 3D printing technology on robotic design and manufacturing.
• Investigating the use of robotics in hazardous waste management and disposal.
• The effectiveness of swarm robotics in environmental monitoring and data collection.
• Analyzing the ethical and legal aspects of deploying autonomous weapon systems.
• The role of robotics in enhancing logistics and supply chain management.
• Investigating the potential of robotic process automation in banking and finance.
• The impact of robotics and automation on the future of urban planning and smart cities.

Robotics Research Ideas (Continued)

• Developing underwater robots for marine biodiversity conservation and research.
• Analyzing the challenges of integrating AI and robotics in the educational sector.
• The role of robotics in advancing precision medicine and personalized healthcare.
• Investigating the social implications of widespread adoption of service robots.
• The impact of automation on productivity and efficiency in the food industry.
• Analyzing human psychological responses to interaction with advanced robots.
• The effectiveness of robotic assistants in enhancing the retail customer experience.
• Investigating the use of automation in streamlining media and entertainment production.
• The role of robotics in preserving cultural heritage and archeological sites.
• Analyzing the potential of robotics in addressing environmental pollution and climate change.
• The impact of cyber-physical systems on the evolution of smart manufacturing.
• Investigating the role of robotics in non-invasive medical diagnostics and screening.
• The effectiveness of robotic technologies in construction and infrastructure development.
• Analyzing the challenges of energy management and sustainability in robotics.
• The role of AI and robotics in advancing space exploration and satellite deployment.
• Investigating the application of robotics in textile and garment manufacturing.
• The impact of automation on the dynamics of global trade and economic growth.
• Analyzing the role of robotics in enhancing sports training and athlete performance.
• The effectiveness of robotic systems in large-scale environmental restoration projects.
• Investigating the potential of AI-driven robots in personalized content creation and delivery.
• The role of robotics in improving safety and efficiency in mining operations.
• Analyzing the impact of robotic automation on customer service and support.
• The effectiveness of autonomous robotic systems in utility and infrastructure inspection.
• Investigating the role of robotics in enhancing border security and surveillance.
• The impact of robotic and automated technologies on future transportation systems.

Recent Studies: Robotics & Automation

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual robotics and automation-related studies to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• A Comprehensive Survey on Robotics and Automation in Various Industries (Jeyakumar K, 2022)
• Dual-Material 3D-Printed PaCoMe-Like Fingers for Flexible Biolaboratory Automation (Zwirnmann et al., 2023)
• Robotic Process Automation (RPA) Adoption: A Systematic Literature Review (Costa et al., 2022)
• Analysis of the Conditions Influencing the Assimilation of Robotic Process Automation by Enterprises (Sobczak, 2022)
• Using RPA for Performance Monitoring of Dynamic SHM Applications (Atencio et al., 2022)
• When Harry, the Human, Met Sally, the Software Robot: Metaphorical Sensemaking and Sensegiving around an Emergent Digital Technology (Techatassanasoontorn et al., 2023)
• Model-driven Engineering and Simulation of Industrial Robots with ROS (Hoppe & Hoffschulte, 2022)
• RPA Bot to Automate Students Marks Storage Process (Krishna, 2022)
• Intelligent Process Automation and Business Continuity: Areas for Future Research (Brás et al., 2023)
• Enabling the Gab Between RPA and Process Mining: User Interface Interactions Recorder (Choi et al., 2022)
• An Electroadhesive Paper Gripper With Application to a Document-Sorting Robot (Itoh et al., 2022)
• A systematic literature review on Robotic Process Automation security (Gajjar et al., 2022)
• Teaching Industrial Robot Programming Using FANUC ROBOGUIDE and iRVision Software (Coletta & Chauhan, 2022)
• Industrial Automation and Robotics (Kumar & Babu, 2022)
• Process & Software Selection for Robotic Process Automation (RPA) (Axmann & Harmoko, 2022)
• Robotic Process Automation: A Literature-Based Research Agenda (Plattfaut & Borghoff, 2022)
• Automated Testing of RPA Implementations (Sankpal, 2022) Template-Based Category-Agnostic Instance Detection for Robotic Manipulation (Hu et al., 2022)
• Robotic Process Automation in Smart System Platform: A Review (Falih et al., 2022)
• MANAGEMENT CONSIDERATIONS FOR ROBOTIC PROCESS AUTOMATION IMPLEMENTATIONS IN DIGITAL INDUSTRIES (Stamoulis, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Currently Available Theses Topics

We offer these current topics directly for Bachelor and Master students at TU Darmstadt who can feel free to DIRECTLY contact the thesis advisor if you are interested in one of these topics. Excellent external students from another university may be accepted but are required to first email Jan Peters before contacting any other lab member for a thesis topic. Note that we cannot provide funding for any of these theses projects.

We highly recommend that you do either our robotics and machine learning lectures ( Robot Learning , Statistical Machine Learning ) or our colleagues ( Grundlagen der Robotik , Probabilistic Graphical Models and/or Deep Learning). Even more important to us is that you take both Robot Learning: Integrated Project, Part 1 (Literature Review and Simulation Studies) and Part 2 (Evaluation and Submission to a Conference) before doing a thesis with us.

In addition, we are usually happy to devise new topics on request to suit the abilities of excellent students. Please DIRECTLY contact the thesis advisor if you are interested in one of these topics. When you contact the advisor, it would be nice if you could mention (1) WHY you are interested in the topic (dreams, parts of the problem, etc), and (2) WHAT makes you special for the projects (e.g., class work, project experience, special programming or math skills, prior work, etc.). Supplementary materials (CV, grades, etc) are highly appreciated. Of course, such materials are not mandatory but they help the advisor to see whether the topic is too easy, just about right or too hard for you.

Only contact *ONE* potential advisor at the same time! If you contact a second one without first concluding discussions with the first advisor (i.e., decide for or against the thesis with her or him), we may not consider you at all. Only if you are super excited for at most two topics send an email to both supervisors, so that the supervisors are aware of the additional interest.

FOR FB16+FB18 STUDENTS: Students from other depts at TU Darmstadt (e.g., ME, EE, IST), you need an additional formal supervisor who officially issues the topic. Please do not try to arrange your home dept advisor by yourself but let the supervising IAS member get in touch with that person instead. Multiple professors from other depts have complained that they were asked to co-supervise before getting contacted by our advising lab member.

NEW THESES START HERE

Blending Deep Generative Models using Stochastic Optimization

Topic: This Master Thesis aims to explore the blending of deep generative models using stochastic optimization techniques [1], focusing on reactive motion generation for robotics. The research will encompass the training of deep generative models, such as Score-based [2], or Flow-based models, specifically utilizing the JAX framework for efficient computation. A significant part of the thesis will involve deriving a mixture of experts algorithm, which leverages these trained generative models in combination with other manually specified objectives to enhance the performance of the motion generator. This integration aims to create more adaptive and responsive robotic behaviors in dynamic environments, offering a substantial advancement over existing methods.

Requirements

• Strong Python programming skills
• Knowledge in Machine Learning
• Experience with deep learning libraries and JAX is a plus

Interested students can apply by sending an e-mail to [email protected] and attaching the documents mentioned below:

• Curriculum Vitae
• Motivation letter explaining why you would like to work on this topic and why you are the perfect candidate

References [1] Hansel, K.; Urain, J.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending as Inference for Reactive Robot Control, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. [2] Urain, J.; Funk, N.; Peters, J.; Chalvatzaki G (2023). SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion, International Conference on Robotics and Automation (ICRA).

Self-supervised learning of a visual object-centric representation for robotic manipulation

Scope: External Master Thesis 🇫🇷 This master thesis will be conducted with our French partners at Ecole Centrale de Lyon . Possibility of ERASMUS scholarship. Advisor: Alexandre Chapin , Liming Chen , Emmanuel Dellandrea Added: 2024-07-15 Start: ASAP Topic:

Vision-based learning for robotic manipulation often relies on holistic visual scene representations, where the environment is depicted as a single vector. This method is suboptimal for handling diverse scenes and objects in unconstrained environments. Better representations can improve generalization and data efficiency in robotic learning [1]. Inspired by human perception, object-centric representation has been developed to represent environments with multiple vectors, each corresponding to an object's properties [2]. However, these methods mainly use synthetic datasets [3, 4, 5] and struggle with real-world scenarios [6]. With advances in self-supervised learning for vision models [7, 8], which show promise for object discovery, we propose pre-training an object-centric representation using self-supervised methods to scale to real-world scenarios. This thesis will focus on: Developing and training an object-centric self-supervised model on a real-world dataset. Pre-training the model on a real-world robotic dataset. Applying the pre-trained model to visual-based robotic manipulation tasks.

Interested students can apply by sending the required documents to [email protected] and attaching the required documents mentioned below.

• Experience with the Pytorch library

Preferred Qualifications

• Prior experience in Computer Vision and/or Robotics is preferred
• Use of distributed environment for learning of models (SLURM)
• Knowledge on recent self-supervised learning methods for vision [7, 8]

Required Documents

References [1] O. Kroemer et al. “A review of robot learning for manipulation: Challenges, representations, and algorithms” (2019) [2] F. Locatello et al. “Object-centric learning with Slot Attention” (2020) [3] G. Singh et al. “Illiterate DALL-E learns to compose” (2021) [4] T. Kipf et al. “Conditional object-centric learning from video” (2022) [5] G. Singh et al. “Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos” (2022) [6] Z. Wu et al. “SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models” (2023) [7] M. Caron et al. “Emerging Properties in Self-Supervised Vision Transformers” (2021) [8] O. J. Hénaff et al. “Object discovery and representation networks” (2022)

Imitation Learning for High-Speed Robot Air Hockey

Scope: Master thesis Advisor: Puze Liu and Julen Urain De Jesus Start: ASAP Topic:

High-speed reactive motion is one of the fundamental capabilities of robots to achieve human-level behavior. Optimization-based methods suffer from real-time requirement when the problem is non-convex and contains constraints. Reinforcement learning requires extensive reward engineering to achieve the desired performance. Imitation learning, on the other hand, gathers human knowledge directly from data collection and enables robots to learn natural movements efficiently. In this paper, we explore how imitation learning can be performed in a complex robot Air Hockey Task. The robot needs to learn not only low-level skills, but also high-level tactics from human demonstrations.

• Knowledge in Machine Learning / Supervised Learning
• Good Knowledge in Robotics
• Experience with deep learning libraries is a plus

References * Chi, Cheng, et al. "Diffusion policy: Visuomotor policy learning via action diffusion." arXiv preprint arXiv:2303.04137 (2023). * Liu, Puze, et al. "Robot reinforcement learning on the constraint manifold." Conference on Robot Learning. PMLR (2022). * Pan, Yunpeng, et al. "Imitation learning for agile autonomous driving." The International Journal of Robotics Research 39.2-3 (2020). Interested students can apply by sending an e-mail to [email protected] and attaching the required documents mentioned above.

Walk your network: investigating neural network’s location in Q-learning methods.

Scope: Master thesis Advisor: Theo Vincent Start: Flexible Topic:

Q-learning methods are at the heart of Reinforcement Learning. They have been shown to outperform humans on some complex tasks such as playing video games [1]. In robotics, where the action space is in most cases continuous, actor-critic methods are relying on Q-learning methods to learn the critic [2]. Although Q-learning methods have been extensively studied in the past, little focus has been placed on the way the online neural network is exploring the space of Q functions. Most approaches focus on crafting a loss that would make the agent learn better policies [3]. Here, we offer a thesis that focuses on the position of the online Q neural network in the space of Q functions. The student will first investigate this idea on simple problems before comparing the performance to strong baselines such as DQN or REM [1, 4] on Atari games. Depending on the result, the student might as well get into MuJoCo and compare the results with SAC [2]. The student will be welcome to propose some ideas as well.

Highly motivated students can apply by sending an email to [email protected] . Please attach your CV, a grade sheet and clearly state why you are interested in this topic. Students who have followed the Reinforcement Learning or Robot Learning course will be prioritized.

• Knowledge in Reinforcement Learning

References [1] Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." nature 518.7540 (2015): 529-533. [2] Haarnoja, Tuomas, et al. "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor." International conference on machine learning. PMLR, 2018. [3] Hessel, Matteo, et al. "Rainbow: Combining improvements in deep reinforcement learning." Proceedings of the AAAI conference on artificial intelligence. Vol. 32. No. 1. 2018. [4] Agarwal, R., Schuurmans, D. & Norouzi, M.. (2020). An Optimistic Perspective on Offline Reinforcement Learning International Conference on Machine Learning (ICML).

Co-optimizing Hand and Action for Robotic Grasping of Deformable objects

This project aims to advance deformable object manipulation by co-optimizing robot gripper morphology and control policies. The project will involve utilizing existing simulation environments for deformable object manipulation [2] and implementing a method to jointly optimize gripper morphology and grasp policies within the simulation.

Required Qualification:

• Familiarity with deep learning libraries such as PyTorch or Tensorflow

Preferred Qualification:

• Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and "Robot Learning"

Application Requirements:

Interested students can apply by sending an e-mail to [email protected] and attaching the required documents mentioned above.

References: [1] Xu, Jie, et al. "An End-to-End Differentiable Framework for Contact-Aware Robot Design." Robotics: Science & Systems. 2021. [2] Huang, Isabella, et al. "DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets." arXiv preprint arXiv:2303.16138 (2023).

Geometry-Aware Diffusion Models for Robotics

In this thesis, you will work on developing an imitation learning algorithm using diffusion models for robotic manipulation tasks, such as the ones in [2, 3, 4], but taking into account the geometry of the task space.

If this sounds interesting, please send an email to [email protected] and [email protected] , and possibly attach your CV, highlighting the relevant courses you took in robotics and machine learning.

What's in it for you:

• You get to work on an exciting topic at the intersection of deep-learning and robotics
• We will supervise you closely throughout your thesis
• Depending on the results, we will aim for an international conference publication

Requirements:

• Be motivated -- we will support you a lot, but we expect you to contribute a lot too
• Robotics knowledge
• Experience setting up deep learning pipelines -- from data collection, architecture design, training, and evaluation
• PyTorch -- especially experience writing good parallelizable code (i.e., runs fast in the GPU)

References: [1] https://arxiv.org/abs/2112.10752 [2] https://arxiv.org/abs/2308.01557 [3] https://arxiv.org/abs/2209.03855 [4] https://arxiv.org/abs/2303.04137 [5] https://arxiv.org/abs/2205.09991

Learning Latent Representations for Embodied Agents

Interested students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.

• Experience with TensorFlow/PyTorch
• Familiarity with core Machine Learning topics
• Experience programming/controlling robots (either simulated or real world)
• Knowledgeable about different robot platforms (quadrupeds and bipedal robots)
• Resume / CV
• Cover letter explaining why this topic fits you well and why you are an ideal candidate

References: [1] Ho and Ermon. "Generative adversarial imitation learning" [2] Arenz, et al. "Efficient Gradient-Free Variational Inference using Policy Search"

Characterizing Fear-induced Adaptation of Balance by Inverse Reinforcement Learning

Interested students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.

• Basic knowledge of reinforcement learning
• Hand-on experience with reinforcement learning or inverse reinforcement learning
• Cognitive science background

References: [1] Maki, et al. "Fear of Falling and Postural Performance in the Elderly" [2] Davis et al. "The relationship between fear of falling and human postural control" [3] Ho and Ermon. "Generative adversarial imitation learning"

Timing is Key: CPGs for regularizing Quadruped Gaits learned with DRL

To tackle this problem we want to utilize Central Pattern Generators (CPGs), which can generate timings for ground contacts for the four feet. The policy gets rewarded for complying with the contact patterns of the CPGs. This leads to a straightforward way of regularizing and steering the policy to a natural gait without posing too strong restrictions on it. We first want to manually find fitting CPG parameters for different gait velocities and later move to learning those parameters in an end-to-end fashion.

Highly motivated students can apply by sending an E-Mail to [email protected] and attaching the required documents mentioned below.

Minimum Qualification:

• Good Python programming skills
• Basic knowledge of the PyTorch library
• Basic knowledge of Reinforcement Learning
• Good knowledge of the PyTorch library
• Basic knowledge of the MuJoCo simulator

References: [1] Cheng, Xuxin, et al. "Extreme Parkour with Legged Robots."

Damage-aware Reinforcement Learning for Deformable and Fragile Objects

Goal of this thesis will be the development and application of a model-based reinforcement learning method on real robots. Your tasks will include: 1. Setting up a simulation environment for deformable object manipulation 2. Utilizing existing models for stress and deformability prediction[1] 3. Implementing a reinforcement learning method to work in simulation and, if possible, on the real robot methods.

If you are interested in this thesis topic and believe you possess the necessary skills and qualifications, please submit your application, including a resume and a brief motivation letter explaining your interest and relevant experience. Please send your application to [email protected].

Required Qualification :

• Enthusiasm for and experience in robotics, machine learning, and simulation
• Strong programming skills in Python

Desired Qualification :

• Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and (optionally) "Robot Learning"

References: [1] Huang, I., Narang, Y., Bajcsy, R., Ramos, F., Hermans, T., & Fox, D. (2023). DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets. arXiv preprint arXiv:2303.16138.

Imitation Learning meets Diffusion Models for Robotics

The objective of this thesis is to build upon prior research [2, 3] to establish a connection between Diffusion Models and Imitation Learning. We aim to explore how to exploit Diffusion Models and improve the performance of Imitation learning algorithms that interact with the world.

We welcome highly motivated students to apply for this opportunity by sending an email expressing their interest to Firas Al-Hafez ( [email protected] ) Julen Urain ( [email protected] ). Please attach your letter of motivation and CV, and clearly state why you are interested in this topic and why you are the ideal candidate for this position.

Required Qualification : 1. Strong Python programming skills 2. Basic Knowledge in Imitation Learning 3. Interest in Diffusion models, Reinforcement Learning

Desired Qualification : 1. Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and/or "Reinforcement Learning: From Fundamentals to the Deep Approaches"

References: [1] Song, Yang, and Stefano Ermon. "Generative modeling by estimating gradients of the data distribution." Advances in neural information processing systems 32 (2019). [2] Ho, Jonathan, and Stefano Ermon. "Generative adversarial imitation learning." Advances in neural information processing systems 29 (2016). [3] Garg, D., Chakraborty, S., Cundy, C., Song, J., & Ermon, S. (2021). Iq-learn: Inverse soft-q learning for imitation. Advances in Neural Information Processing Systems, 34, 4028-4039. [4] Chen, R. T., & Lipman, Y. (2023). Riemannian flow matching on general geometries. arXiv preprint arXiv:2302.03660.

• Be extremely motivated -- we will support you a lot, but we expect you to contribute a lot too

Scaling Behavior Cloning to Humanoid Locomotion

Scope: Bachelor / Master thesis Advisor: Joe Watson Added: 2023-10-07 Start: ASAP Topic: In a previous project [1], I found that behavior cloning (BC) was a surprisingly poor baseline for imitating humanoid locomotion. I suspect the issue may lie in the challenges of regularizing high-dimensional regression.

The goal of this project is to investigate BC for humanoid imitation, understand the scaling issues present, and evaluate possible solutions, e.g. regularization strategies from the regression literature.

The project will be building off Google Deepmind's Acme library [2], which has BC algorithms and humanoid demonstration datasets [3] already implemented, and will serve as the foundation of the project.

To apply, email [email protected] , ideally with a CV and transcript so I can assess your suitability.

• Experience, interest and enthusiasm for the intersection of robot learning and machine learning
• Experience with Acme and JAX would be a benefit, but not necessary

References: [1] https://arxiv.org/abs/2305.16498 [2] https://github.com/google-deepmind/acme [3] https://arxiv.org/abs/2106.00672

Robot Gaze for Communicating Collision Avoidance Intent in Shared Workspaces

Scope: Bachelor/Master thesis Advisor: Alap Kshirsagar , Dorothea Koert Added: 2023-09-27 Start: ASAP

Topic: In order to operate close to non-experts, future robots require both an intuitive form of instruction accessible to lay users and the ability to react appropriately to a human co-worker. Instruction by imitation learning with probabilistic movement primitives (ProMPs) [1] allows capturing tasks by learning robot trajectories from demonstrations including the motion variability. However, appropriate responses to human co-workers during the execution of the learned movements are crucial for fluent task execution, perceived safety, and subjective comfort. To facilitate such appropriate responsive behaviors in human-robot interaction, the robot needs to be able to react to its human workspace co-inhabitant online during the execution. Also, the robot needs to communicate its motion intent to the human through non-verbal gestures such as eye and head gazes [2][3]. In particular for humanoid robots, combining motions of arms with expressive head and gaze directions is a promising approach that has not yet been extensively studied in related work.

Goals of the thesis:

• Develop a method to combine robot head/gaze motion with ProMPs for online collision avoidance
• Implement the method on a Franka-Emika Panda Robot
• Evaluate and compare the implemented behaviors in a study with human participants

Highly motivated students can apply by sending an email to [email protected]. Please attach your CV and transcript, and clearly state your prior experiences and why you are interested in this topic.

• Strong Programming Skills in python
• Prior experience with Robot Operating System (ROS) and user studies would be beneficial
• Strong motivation for human-centered robotics including design and implementation of a user study

References : [1] Koert, Dorothea, et al. "Learning intention aware online adaptation of movement primitives." IEEE Robotics and Automation Letters 4.4 (2019): 3719-3726. [2] Admoni, Henny, and Brian Scassellati. "Social eye gaze in human-robot interaction: a review." Journal of Human-Robot Interaction 6.1 (2017): 25-63. [3] Lemasurier, Gregory, et al. "Methods for expressing robot intent for human–robot collaboration in shared workspaces." ACM Transactions on Human-Robot Interaction (THRI) 10.4 (2021): 1-27.

Tactile Sensing for the Real World

Topic: Tactile sensing is a crucial sensing modality that allows humans to perform dexterous manipulation[1]. In recent years, the development of artificial tactile sensors has made substantial progress, with current models relying on cameras inside the fingertips to extract information about the points of contact [2]. However, robotic tactile sensing is still a largely unsolved topic despite these developments. A central challenge of tactile sensing is the extraction of usable representations of sensor readings, especially since these generally contain an incomplete view of the environment.

Recent model-based reinforcement learning methods like Dreamer [3] leverage latent state-space models to reason about the environment from partial and noisy observations. However, more work has yet to be done to apply such methods to real-world manipulation tasks. Hence, this thesis will explore whether Dreamer can solve challenging real-world manipulation tasks by leveraging tactile information. Initial results suggest that tasks like peg-in-a-hole can indeed be solved with Dreamer in simulation (see figure above), but the applicability of this method in the real world has yet to be shown.

In this work, you will work with state-of-the-art hardware and compute resources on a hot research topic with the option of publishing your work at a scientific conference.

Highly motivated students can apply by sending an email to [email protected]. Please attach a transcript of records and clearly state your prior experiences and why you are interested in this topic.

• Ideally experience with deep learning libraries like JAX or PyTorch
• Experience with reinforcement learning is a plus
• Experience with Linux

References [1] 2S Match Anest2, Roland Johansson Lab (2005), https://www.youtube.com/watch?v=HH6QD0MgqDQ [2] Gelsight Inc., Gelsight Mini, https://www.gelsight.com/gelsightmini/ [3] Hafner, D., Lillicrap, T., Ba, J., & Norouzi, M. (2019). Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603.

Large Vision-Language Neural Networks for Open-Vocabulary Robotic Manipulation

Robots are expected to soon leave their factory/laboratory enclosures and operate autonomously in everyday unstructured environments such as households. Semantic information is especially important when considering real-world robotic applications where the robot needs to re-arrange objects as per a set of language instructions or human inputs (as shown in the figure). Many sophisticated semantic segmentation networks exist [1]. However, a challenge when using such methods in the real world is that the semantic classes rarely align perfectly with the language input received by the robot. For instance, a human language instruction might request a ‘glass’ or ‘water’, but the semantic classes detected might be ‘cup’ or ‘drink’.

Nevertheless, with the rise of large language and vision-language models, we now have capable segmentation models that do not directly predict semantic classes but use learned associations between language queries and classes to give us ’open-vocabulary’ segmentation [2]. Some models are especially powerful since they can be used with arbitrary language queries.

In this thesis, we aim to build on advances in 3D vision-based robot manipulation and large open-vocabulary vision models [2] to build a full pick-and-place pipeline for real-world manipulation. We also aim to find synergies between scene reconstruction and semantic segmentation to determine if knowing the object semantics can aid the reconstruction of the objects and, in turn, aid manipulation.

Highly motivated students can apply by sending an e-mail expressing their interest to Snehal Jauhri (email: [email protected]) or Ali Younes (email: [email protected]), attaching your letter of motivation and possibly your CV.

Topic in detail : Thesis_Doc.pdf

Requirements: Enthusiasm, ambition, and a curious mind go a long way. There will be ample supervision provided to help the student understand basic as well as advanced concepts. However, prior knowledge of computer vision, robotics, and Python programming would be a plus.

References: [1] Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick, “Detectron2”, https://github.com/facebookresearch/detectron2 , 2019. [2] F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu, “Open-vocabulary semantic segmentation with mask-adapted clip,” in CVPR, 2023, pp. 7061–7070, https://github.com/facebookresearch/ov-seg

Dynamic Tiles for Deep Reinforcement Learning

Linear approximators in Reinforcement Learning are well-studied and come with an in-depth theoretical analysis. However, linear methods require defining a set of features of the state to be used by the linear approximation. Unfortunately, the feature construction process is a particularly problematic and challenging task. Deep Reinforcement learning methods have been introduced to mitigate the feature construction problem: these methods do not require handcrafted features, as features are extracted automatically by the network during learning, using gradient descent techniques.

In simple reinforcement learning tasks, however, it is possible to use tile coding as features: Tiles are simply a convenient discretization of the state space that allows us to easily control the generalization capabilities of the linear approximator. The objective of this thesis is to design a novel algorithm for automatic feature extraction that generates a set of features similar to tile coding, but that can arbitrarily partition the state space and deal with arbitrary complex state space, such as images. The idea is to combine the feature extraction problem directly with Linear Reinforcement Learning methods, defining an algorithm that is able both to have the theoretical guarantees and good convergence properties of these methods and the flexibility of Deep Learning approaches.

• Curriculum Vitae (CV);
• A motivation letter explaining the reason for applying for this thesis and academic/career objectives.

Minimum knowledge

• Good Python programming skills;
• Basic knowledge of Reinforcement Learning.

Preferred knowledge

• Knowledge of the PyTorch library;
• Knowledge of the Atari environments (ale-py library).
• Knowledge of the MushroomRL library.

Accepted candidate will

• Define a generalization of tile coding working with an arbitrary input set (including images);
• Design a learning algorithm to adapt the tiles using data of interaction with the environment;
• Combine feature learning with standard linear methods for Reinforcement Learning;
• Verify the novel methodology in simple continuous state and discrete actions environments;
• (Optionally) Extend the experimental analysis to the Atari environment setting.

Deep Learning Meets Teleoperation: Constructing Learnable and Stable Inductive Guidance for Shared Control

This work considers policies as learnable inductive guidance for shared control. In particular, we use the class of Riemannian motion policies [3] and consider them as differentiable optimization layers [4]. We analyze (i) if RMPs can be pre-trained by learning from demonstrations [5] or reinforcement learning [6] given a specific context; (ii) and subsequently employed seamlessly for human-guided teleoperation thanks to their physically consistent properties, such as stability [3]. We believe this step eliminates the laborious process of constructing complex policies and leads to improved and generalizable shared control architectures.

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] and [email protected] , attaching your letter of motivation and possibly your CV.

• Experience with deep learning libraries (in particular Pytorch)
• Knowledge in reinforcement learning and/or machine learning

References: [1] Niemeyer, Günter, et al. "Telerobotics." Springer handbook of robotics (2016); [2] Selvaggio, Mario, et al. "Autonomy in physical human-robot interaction: A brief survey." IEEE RAL (2021); [3] Cheng, Ching-An, et al. "RMP flow: A Computational Graph for Automatic Motion Policy Generation." Springer (2020); [4] Jaquier, Noémie, et al. "Learning to sequence and blend robot skills via differentiable optimization." IEEE RAL (2022); [5] Mukadam, Mustafa, et al. "Riemannian motion policy fusion through learnable lyapunov function reshaping." CoRL (2020); [6] Xie, Mandy, et al. "Neural geometric fabrics: Efficiently learning high-dimensional policies from demonstration." CoRL (2023).

Dynamic symphony: Seamless human-robot collaboration through hierarchical policy blending

This work focuses on arbitration between the user and assistive policy, i.e., shared autonomy. Various works allow the user to influence the dynamic behavior explicitly and, therefore, could not satisfy stability guarantees [3]. We pursue the idea of formulating arbitration as a trajectory-tracking problem that implicitly considers the user's desired behavior as an objective [4]. Therefore, we extend the work of Hansel et al. [5], who employed probabilistic inference for policy blending in robot motion control. The proposed method corresponds to a sampling-based online planner that superposes reactive policies given a predefined objective. This method enables the user to implicitly influence the behavior without injecting energy into the system, thus satisfying stability properties. We believe this step leads to an alternative view of shared autonomy with an improved and generalizable framework.

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] or [email protected] , attaching your letter of motivation and possibly your CV.

References: [1] Niemeyer, Günter, et al. "Telerobotics." Springer handbook of robotics (2016); [2] Selvaggio, Mario, et al. "Autonomy in physical human-robot interaction: A brief survey." IEEE RAL (2021); [3] Dragan, Anca D., and Siddhartha S. Srinivasa. "A policy-blending formalism for shared control." IJRR (2013); [4] Javdani, Shervin, et al. "Shared autonomy via hindsight optimization for teleoperation and teaming." IJRR (2018); [5] Hansel, Kay, et al. "Hierarchical Policy Blending as Inference for Reactive Robot Control." IEEE ICRA (2023).

Feeling the Heat: Igniting Matches via Tactile Sensing and Human Demonstrations

In this thesis, we want to investigate the effectiveness of vision-based tactile sensors for solving dynamic tasks (igniting matches). Since the whole task is difficult to simulate, we directly collect real-world data to learn policies from the human demonstrations [2,3]. We believe that this work is an important step towards more advanced tactile skills.

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] and [email protected] , attaching your letter of motivation and possibly your CV.

• Good knowledge of Python
• Prior experience with real robots and Linux is a plus

References: [1] https://www.youtube.com/watch?v=HH6QD0MgqDQ [2] Learning Compliant Manipulation through Kinesthetic and Tactile Human-Robot Interaction; Klas Kronander and Aude Billard. [3] https://www.youtube.com/watch?v=jAtNvfPrKH8

Inverse Reinforcement Learning for Neuromuscular Control of Humanoids

Within this thesis, the problems of learning from observations and efficient exploration in overactued systems should be addressed. Regarding the former, novel methods incorporating inverse dynamics models into the inverse reinforcement learning problem [1] should be adapted and applied. To address the problem of efficient exploration in overactuted systems, two approaches should be implemented and compared. The first approach uses a handcrafted action space, which disables and modulates actions in different phases of the gait based on biomechanics knowledge [2]. The second approach uses a stateful policy to incorporate an inductive bias into the policy [3]. The thesis will be supervised in conjunction with Guoping Zhao ( [email protected] ) from the locomotion lab.

Highly motivated students can apply by sending an e-mail expressing their interest to Firas Al-Hafez ( [email protected] ), attaching your letter of motivation and possibly your CV. Try to make clear why you would like to work on this topic, and why you would be the perfect candidate for the latter.

Required Qualification : 1. Strong Python programming skills 2. Knowledge in Reinforcement Learning 3. Interest in understanding human locomotion

Desired Qualification : 1. Hands-on experience on robotics-related RL projects 2. Prior experience with different simulators 3. Attendance of the lectures "Statistical Machine Learning", "Computational Engineering and Robotics" and/or "Reinforcement Learning: From Fundamentals to the Deep Approaches"

References: [1] Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR). [2] Ong CF; Geijtenbeek T.; Hicks JL; Delp SL (2019) Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations. PLoS Computational Biology [3] Srouji, M.; Zhang, J:;Salakhutdinow, R. (2018) Structured Control Nets for Deep Reinforcement Learning, International Conference on Machine Learning (ICML)

Robotic Tactile Exploratory Procedures for Identifying Object Properties

Goals of the thesis

• Literature review of robotic EPs for identifying object properties [2,3,4]
• Develop and implement robotic EPs for a Digit tactile sensor
• Compare performance of robotic EPs with human EPs

Desired Qualifications

• Interested in working with real robotic systems
• Python programming skills

Literature [1] Lederman and Klatzky, “Haptic perception: a tutorial” [2] Seminara et al., “Active Haptic Perception in Robots: A Review” [3] Chu et al., “Using robotic exploratory procedures to learn the meaning of haptic adjectives” [4] Kerzel et al., “Neuro-Robotic Haptic Object Classification by Active Exploration on a Novel Dataset”

Scaling learned, graph-based assembly policies

• scaling our previous methods to incorporate mobile manipulators or the Kobo bi-manual manipulation platform. The increased workspace of both would allow for handling a wider range of objects
• [2] has shown more powerful, yet, it includes running a MILP for every desired structure. Thus another idea could be to investigate approaches aiming to approximate this solution
• adapting the methods to handle more irregular-shaped objects / investigate curriculum learning

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] , attaching your letter of motivation and possibly your CV.

• Experience with deep learning libraries (in particular Pytorch) is a plus
• Experience with reinforcement learning / having taken Robot Learning is also a plus

References: [1] Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction; Niklas Funk et al. [2] Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery; Niklas Funk et al. [3] Structured agents for physical construction; Victor Bapst et al.

Long-Horizon Manipulation Tasks from Visual Imitation Learning (LHMT-VIL): Algorithm

The proposed architecture can be broken down into the following sub-tasks: 1. Multi-object 6D pose estimation from video: Identify the object 6D poses in each video frame to generate the object trajectories 2. Action segmentation from video: Classify the action being performed in each video frame 3. High-level task representation learning: Learn the sequence of robotic movement primitives with the associated object poses such that the robot completes the demonstrated task 4. Low-level movement primitives: Create a database of low-level robotic movement primitives which can be sequenced to solve the long-horizon task

Desired Qualification: 1. Strong Python programming skills 2. Prior experience in Computer Vision and/or Robotics is preferred

Long-Horizon Manipulation Tasks from Visual Imitation Learning (LHMT-VIL): Dataset

During the project, we will create a large-scale dataset of videos of humans demonstrating industrial assembly sequences. The dataset will contain information of the 6D poses of the objects, the hand and body poses of the human, the action sequences among numerous other features. The dataset will be open-sourced to encourage further research on VIL.

[1] F. Sener, et al. "Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities". CVPR 2022. [2] P. Sharma, et al. "Multiple Interactions Made Easy (MIME) : Large Scale Demonstrations Data for Imitation." CoRL, 2018.

Adaptive Human-Robot Interactions with Human Trust Maximization

• Good knowledge of Python and/or C++;
• Good knowledge in Robotics and Machine Learning;
• Good knowledge of Deep Learning frameworks, e.g, PyTorch;

References: [1] Xu, Anqi, and Gregory Dudek. "Optimo: Online probabilistic trust inference model for asymmetric human-robot collaborations." ACM/IEEE HRI, IEEE, 2015; [2] Kwon, Minae, et al. "When humans aren’t optimal: Robots that collaborate with risk-aware humans." ACM/IEEE HRI, IEEE, 2020; [3] Chen, Min, et al. "Planning with trust for human-robot collaboration." ACM/IEEE HRI, IEEE, 2018; [4] Poole, Ben et al. “On variational bounds of mutual information”. ICML, PMLR, 2019.

Causal inference of human behavior dynamics for physical Human-Robot Interactions

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] , attaching your a letter of motivation and possibly your CV.

• Good knowledge of Robotics;
• Good knowledge of Deep Learning frameworks, e.g, PyTorch
• Li, Q., Chalvatzaki, G., Peters, J., Wang, Y., Directed Acyclic Graph Neural Network for Human Motion Prediction, 2021 IEEE International Conference on Robotics and Automation (ICRA).
• Löwe, S., Madras, D., Zemel, R. and Welling, M., 2020. Amortized causal discovery: Learning to infer causal graphs from time-series data. arXiv preprint arXiv:2006.10833.
• Yang, W., Paxton, C., Mousavian, A., Chao, Y.W., Cakmak, M. and Fox, D., 2020. Reactive human-to-robot handovers of arbitrary objects. arXiv preprint arXiv:2011.08961.

Incorporating First and Second Order Mental Models for Human-Robot Cooperative Manipulation Under Partial Observability

Scope: Master Thesis Advisor: Dorothea Koert , Joni Pajarinen Added: 2021-06-08 Start: ASAP

The ability to model the beliefs and goals of a partner is an essential part of cooperative tasks. While humans develop theory of mind models for this aim already at a very early age [1] it is still an open question how to implement and make use of such models for cooperative robots [2,3,4]. In particular, in shared workspaces human robot collaboration could potentially profit from the use of such models e.g. if the robot can detect and react to planned human goals or a human's false beliefs during task execution. To make such robots a reality, the goal of this thesis is to investigate the use of first and second order mental models in a cooperative manipulation task under partial observability. Partially observable Markov decision processes (POMDPs) and interactive POMDPs (I-POMDPs) [5] define an optimal solution to the mental modeling task and may provide a solid theoretical basis for modelling. The thesis may also compare related approaches from the literature and setup an experimental design for evaluation with the bi-manual robot platform Kobo.

Highly motivated students can apply by sending an e-mail expressing your interest to [email protected] attaching your CV and transcripts.

References:

• Wimmer, H., & Perner, J. Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception (1983)
• Sandra Devin and Rachid Alami. An implemented theory of mind to improve human-robot shared plans execution (2016)
• Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, SM Ali Eslami,and Matthew Botvinick. Machine theory of mind (2018)
• Connor Brooks and Daniel Szafir. Building second-order mental models for human-robot interaction. (2019)
• Prashant Doshi, Xia Qu, Adam Goodie, and Diana Young. Modeling recursive reasoning by humans using empirically informed interactive pomdps. (2010)

Robotics and Intelligent Systems Certificate Program

Topics for research in robotics and intelligent systems.

General areas for study and research:

Chemical and Biological Engineering

• Control of chemical and biological dynamic processes
• Optimal design of systems for material processing
• Manipulation of matter at atomic and molecular scale

Civil and Environmental Engineering

• Structural health monitoring and adaptive structures
• Water resources
• Earthquake detection and protective design
• Remote sensing of natural resources
• Urban planning and engineering

Computer Science

• Theory and practice of computation for physical systems
• Game playing, photo identification, and semantic identification
• Real-time algorithms for measurement, prediction, and control
• Artificial intelligence and machine learning
• Databases, Internet security, and privacy

Electrical Engineering

• Information theory
• Electricity, microelectronics, and electromagnetism
• Digital circuits and computation
• Image processing, face, and character recognition
• Video analysis and manipulation
• Telecommunications networks
• Autonomous vehicles

Mechanical and Aerospace Engineering

• Robotic devices and systems
• Autonomous air, sea, undersea, and land vehicles
• Space exploration and development
• Intelligent control systems
• Biomimetic modeling, dynamics, and control
• Cooperating robots for manufacturing and assembly
• Cooperative control of natural and engineered groups
• Identification of dynamic system models
• Optimal state estimation and control

Operations Research and Financial Engineering

• Intelligent transportation systems
• Financial management and risk analysis
• Dynamic resource management
• Decision science
• Optimal design
• Knowledge, reasoning, and language
• Logic and metaphysics
• Politics and art of robotics and intelligent systems
• Inference, reasoning, problem solving
• Human factors and human-machine interaction
• Human motor control
• Modeling perception
• Neural network (connectionist) modeling of cognitive functions
• Reinforcement learning
• Study of brain function using functional magnetic response imaging, electrical, and optical methods

200+ Robotics Research Topics: Discovering Tomorrow’s Tech

• Post author By admin
• September 15, 2023

Explore cutting-edge robotics research topics and stay ahead of the curve with our comprehensive guide. Discover the latest advancements in the field today.

Robotics research topics are not like any other research topics. In these topics science fiction meets reality and innovation knows no bounds.

In this blog post we are going to explore some of the best robotics research topics that will help you to explore machine learning, artificial intelligence and many more.

Apart from that you will also explore the industries and the future of robotics. Whether you are an experienced engineering or a student of robotics, these project ideas will definitely help you to explore a lot more the dynamic and ever evolving world of robotics. So be ready to explore these topics:-

Table of Contents

Robotics Research Topics

Have a close look at robotics research topics:-

Autonomous Robots

• Development of an Autonomous Delivery Robot for Urban Environments
• Swarm Robotics for Agricultural Crop Monitoring and Maintenance
• Simultaneous Localization and Mapping (SLAM) for Indoor Navigation of Service Robots
• Human-Robot Interaction Study for Improved Robot Assistance in Healthcare
• Self-Driving Car Prototype with Advanced Perception and Decision-Making Algorithms
• Autonomous Aerial Surveillance Drones for Security Applications
• Autonomous Underwater Vehicles (AUVs) for Ocean Exploration
• Robotic Drones for Disaster Response and Search-and-Rescue Missions
• Autonomous Exploration Rover for Planetary Surfaces
• Unmanned Aerial Vehicles (UAVs) for Precision Agriculture and Crop Analysis

Robot Manipulation and Grasping

• Object Recognition and Grasping Planning System for Warehouse Automation
• Cooperative Multi-Robot Manipulation for Assembly Line Tasks
• Tactile Sensing Integration for Precise Robotic Grasping
• Surgical Robot with Enhanced Precision and Control for Minimally Invasive Surgery
• Robotic System for Automated 3D Printing and Fabrication
• Robot-Assisted Cooking System with Object Recognition and Manipulation
• Robotic Arm for Hazardous Materials Handling and Disposal
• Biomechanically Inspired Robotic Finger Design for Grasping
• Multi-Arm Robotic System for Collaborative Manufacturing
• Development of a Dexterous Robotic Hand for Complex Object

Robot Vision and Perception:

• Object Detection and Recognition Framework for Robotic Inspection
• Deep Learning-Based Vision System for Real-time Object Recognition
• Human Activity Recognition Algorithm for Assistive Robots
• Vision-Based Localization and Navigation for Autonomous Vehicles
• Image Processing and Computer Vision for Robotic Surveillance
• Visual Odometry for Precise Mobile Robot Positioning
• Facial Recognition System for Human-Robot Interaction
• 3D Object Reconstruction from 2D Images for Robotic Mapping
• Autonomous Drone with Advanced Vision-Based Obstacle Avoidance
• Development of a Visual SLAM System for Autonomous Indoor navigation.

Human-Robot Collaboration

• Development of Robot Assistants for Elderly Care and Companionship
• Implementation of Collaborative Robots (Cobots) in Manufacturing Processes
• Shared Control Interfaces for Teleoperation of Remote Robots
• Ethics and Social Impact Assessment of Human-Robot Interaction
• Evaluation of User Interfaces for Robotic Assistants in Healthcare
• Human-Centric Design of Robotic Exoskeletons for Enhanced Mobility
• Enhancing Worker Safety in Industrial Settings through Human-Robot Collaboration
• Haptic Feedback Systems for Improved Teleoperation of Remote Robots
• Investigating Human Trust and Acceptance of Autonomous Robots in Daily Life
• Design and Testing of Safe and Efficient Human-Robot Collaborative Workstations

Bio-Inspired Robotics

• Biohybrid Robots Combining Biological and Artificial Components for Unique Functions
• Evolutionary Robotics Algorithms for Robot Behavior Optimization
• Swarm Robotics Inspired by Insect Behavior for Collective Tasks
• Design and Fabrication of Soft Robotics for Flexible and Adaptive Movement
• Biomimetic Robotic Fish for Underwater Exploration
• Biorobotics Research for Prosthetic Limb Design and Control
• Development of a Robotic Pollination System Inspired by Bees
• Bio-Inspired Flying Robots for Agile and Efficient Aerial Navigation
• Bio-Inspired Sensing and Localization Techniques for Robotic Applications
• Development of a Legged Robot with Biomimetic Locomotion Inspired by Animals

Robot Learning and AI

• Transfer Learning Strategies for Robotic Applications in Varied Environments
• Explainable AI Models for Transparent Robot Decision-Making
• Robot Learning from Demonstration (LfD) for Complex Tasks
• Machine Learning Algorithms for Predictive Maintenance of Industrial Robots
• Neural Network-Based Vision System for Autonomous Robot Learning
• Reinforcement Learning for UAV Swarms and Cooperative Flight
• Human-Robot Interaction Studies to Improve Robot Learning
• Natural Language Processing for Human-Robot Communication
• Robotic Systems with Advanced AI for Autonomous Exploration
• Implementation of Reinforcement Learning Algorithms for Robotic Control

Robotics in Healthcare

• Design and Testing of Robotic Prosthetics and Exoskeletons for Enhanced Mobility
• Telemedicine Platform for Remote Robotic Medical Consultations
• Robot-Assisted Rehabilitation System for Physical Therapy
• Simulation-Based Training Environment for Robotic Surgical Skill Assessment
• Humanoid Robot for Social and Emotional Support in Healthcare Settings
• Autonomous Medication Dispensing Robot for Hospitals and Pharmacies
• Wearable Health Monitoring Device with AI Analysis
• Robotic Systems for Elderly Care and Fall Detection
• Surgical Training Simulator with Realistic Haptic Feedback
• Development of a Robotic Surgical Assistant for Minimally Invasive Procedures

Robots in Industry

• Quality Control and Inspection Automation with Robotic Systems
• Risk Assessment and Safety Measures for Industrial Robot Environments
• Human-Robot Collaboration Solutions for Manufacturing and Assembly
• Warehouse Automation with Robotic Pick-and-Place Systems
• Industrial Robot Vision Systems for Quality Assurance
• Integration of Cobots in Flexible Manufacturing Cells
• Robot Grippers and End-Effector Design for Specific Industrial Tasks
• Predictive Maintenance Strategies for Industrial Robot Fleet
• Robotics for Lean Manufacturing and Continuous Improvement
• Robotic Automation in Manufacturing: Process Optimization and Integration

Robots in Space Exploration

• Precise Autonomous Spacecraft Navigation for Deep Space Missions
• Robotics for Satellite Servicing and Space Debris Removal
• Lunar and Martian Surface Exploration with Autonomous Robots
• Resource Utilization and Mining on Extraterrestrial Bodies with Robots
• Design and Testing of Autonomous Space Probes for Interstellar Missions
• Autonomous Space Telescopes for Astronomical Observations
• Robot-Assisted Lunar Base Construction and Maintenance
• Planetary Sample Collection and Return Missions with Robotic Systems
• Biomechanics and Human Factors Research for Astronaut-Robot Collaboration
• Autonomous Planetary Rovers: Mobility and Scientific Exploration

Environmental Robotics

• Environmental Monitoring and Data Collection Using Aerial Drones
• Robotics in Wildlife Conservation: Tracking and Protection of Endangered Species
• Disaster Response Robots: Search, Rescue, and Relief Operations
• Autonomous Agricultural Robots for Sustainable Farming Practices
• Autonomous Forest Fire Detection and Firefighting Robot Systems
• Monitoring and Rehabilitation of Coral Reefs with Robotic Technology
• Air Quality Monitoring and Pollution Detection with Mobile Robot Swarms
• Autonomous River and Marine Cleanup Robots
• Ecological Studies and Environmental Protection with Robotic Instruments
• Development of Underwater Robotic Systems for Ocean Exploration and Monitoring

These project ideas span a wide range of topics within robotics research, offering opportunities for innovation, exploration, and contribution to the field. Researchers, students, and enthusiasts can choose projects that align with their interests and expertise to advance robotics technology and its applications.

Robotics Research Topics for high school students

• Home Robots: Explore how robots can assist in daily tasks at home.
• Medical Robotics: Investigate robots used in surgery and patient care.
• Robotics in Education: Learn about robots teaching coding and science.
• Agricultural Robots: Study robots in farming for planting and monitoring.
• Space Exploration: Discover robots exploring planets and space.
• Environmental Robots: Explore robots in conservation and pollution monitoring.
• Ethical Questions: Discuss the ethical dilemmas in robotics.
• DIY Robot Projects: Build and program robots from scratch.
• Robot Competitions: Participate in exciting robotics competitions.
• Cutting-Edge Innovations: Stay updated on the latest in robotics.

These topics offer exciting opportunities for high school students to delve into robotics research, learning, and creativity.

Easy Robotics Research Topics 

Introduction to robotics.

Explore the basics of robotics, including robot components and their functions.

History of Robotics

Investigate the evolution of robotics from its beginnings to modern applications.

Robotic Sensors

Learn about various sensors used in robots for detecting and measuring data.

Simple Robot Building

Build a basic robot using kits or everyday materials and learn about its components.

Programming a Robot

Experiment with programming languages like Scratch or Blockly to control a robot’s movements.

Robots in Entertainment

Explore how robots are used in the entertainment industry, such as animatronics and robot performers.

Robotics in Toys

Investigate robotic toys and their mechanisms, such as remote-controlled cars and drones.

Robotic Pets

Learn about robotic pets like robot dogs and cats and their interactive features.

Robotics in Science Fiction

Analyze how robots are portrayed in science fiction movies and literature.

Robotic Safety

Explore safety measures and protocols when working with robots to prevent accidents.

These topics provide a gentle introduction to robotics research and are ideal for beginners looking to learn more about this exciting field.

Latest Research Topics in Robotics

The field of robotics is ever-evolving, with a plethora of exciting research topics at the forefront of exploration. Here are some of the latest and most intriguing areas of research in robotics:

Soft Robotics

Soft robots, crafted from flexible materials, can adapt to their surroundings, making them safer for human interaction and ideal for unstructured environments.

Robotic Swarms

Groups of robots working collectively toward a common objective, such as search and rescue missions, disaster relief efforts, and environmental monitoring.

Robotic Exoskeletons

Wearable devices designed to enhance human strength and mobility, offering potential benefits for individuals with disabilities, boosting worker productivity, and aiding soldiers in carrying heavier loads.

Medical Robotics

Robots play a vital role in various medical applications, including surgery, rehabilitation, and drug delivery, enhancing precision, reducing human error, and advancing healthcare practices.

Intelligent Robots

Intelligent robots have the ability to learn and adapt to their surroundings, enabling them to tackle complex tasks and interact naturally with humans.

These are just a glimpse of the thrilling research avenues within robotics. As the field continues to progress, we anticipate witnessing even more groundbreaking advancements and innovations in the years ahead.

What topics are in robotics?

Robotics basics.

Understanding the fundamental concepts of robotics, including robot components, kinematics, and control systems.

Robotics History

Exploring the historical development of robotics and its evolution into a multidisciplinary field.

Robot Sensors

Studying the various sensors used in robots for perception, navigation, and interaction with the environment.

Robot Actuators

Learning about the mechanisms and motors that enable robot movement and manipulation.

Robot Control

Understanding how robots are programmed and controlled, including topics like motion planning and trajectory generation.

Robot Mobility

Examining the different types of robot mobility, such as wheeled, legged, aerial, and underwater robots.

Artificial Intelligence in Robotics

Exploring the role of AI and machine learning in enhancing robot autonomy, decision-making, and adaptability.

Human-Robot Interaction

Investigating how robots can effectively interact with humans, including social and ethical considerations.

Robot Perception

Studying computer vision and other technologies that enable robots to perceive and interpret their surroundings.

Robotic Manipulation

Delving into robot arms, grippers, and manipulation techniques for tasks like object grasping and assembly.

Robot Localization and Mapping

Understanding methods for robot localization (knowing their position) and mapping (creating maps of their environment).

Robotics in Medicine

Exploring the use of robots in surgery, rehabilitation, and medical applications.

Analyzing the role of robots in manufacturing and automation, including industrial robot arms and cobots.

Learning about robots capable of making decisions and navigating autonomously in complex environments.

Robot Ethics

Examining ethical considerations related to robotics, including issues of privacy, safety, and AI ethics.

Exploring robots inspired by nature, such as those mimicking animal locomotion or behavior.

Robotic Applications

Investigating specific applications of robots in fields like agriculture, space exploration, entertainment, and more.

Robotics Research Trends

Staying updated on the latest trends and innovations in the field, such as soft robotics, swarm robotics, and intelligent agents.

These topics represent a broad spectrum of areas within robotics, each offering unique opportunities for research, development, and exploration.

What are your 10 robotics ideas?

Home assistant robot.

Build a robot that can assist with everyday tasks at home, like fetching objects, turning lights on and off, or even helping with cleaning.

Robotics in Agriculture

Create a robot for farming tasks, such as planting seeds, monitoring crop health, or even autonomous weed removal.

Autonomous Delivery Robot

Design a robot capable of delivering packages or groceries autonomously within a neighborhood or urban environment.

Search and Rescue Robot

Develop a robot that can navigate disaster-stricken areas to locate and assist survivors or relay important information to rescuers.

Robot Artist

Build a robot that can create art, whether it’s through painting, drawing, or even sculpture.

Underwater Exploration Robot

Construct a remotely operated vehicle (ROV) for exploring the depths of the ocean and gathering data on marine life and conditions.

Robot for the Elderly

Create a companion robot for the elderly that can provide companionship, reminders for medication, and emergency assistance.

Educational Robot

Design a robot that can teach coding and STEM concepts to children in an engaging and interactive way.

Robotics in Space

Develop a robot designed for space exploration, such as a planetary rover or a robot for asteroid mining.

Design a lifelike robotic pet that can offer companionship and emotional support, especially for those unable to care for a real pet.

These project ideas span various domains within robotics, from practical applications to creative endeavors, offering opportunities for innovation and exploration.

What are the 7 biggest challenges in robotics?

Robot autonomy.

Imagine robots that can think for themselves, make decisions, and navigate complex, ever-changing environments like a seasoned explorer.

Robotic Senses

Picture robots with superhuman perception, able to see, hear, and understand the world around them as well as or even better than humans.

Human-Robot Harmony

Think of robots seamlessly working alongside us, understanding our needs, and being safe, friendly, and helpful companions.

Robotic Hands and Fingers

Envision robots with the dexterity of a skilled surgeon, capable of handling delicate and complex tasks with precision.

Robots on the Move

Imagine robots that can gracefully traverse all kinds of terrain, from busy city streets to rugged mountain paths.

Consider the ethical questions surrounding robots, like privacy, fairness, and the impact on employment, as we strive for responsible and beneficial AI.

Robot Teamwork

Visualize a world where robots from different manufacturers can effortlessly work together, just like a symphony orchestra playing in perfect harmony.

What are the 5 major fields of robotics?

Industrial wizards.

Think of robots working tirelessly on factory floors, welding, assembling, and ensuring top-notch quality in the products we use every day.

Helpful Companions

Imagine robots assisting us in non-industrial settings, from healthcare, where they assist in surgery and rehabilitation, to our homes, where they vacuum our floors and make life a little easier.

Mobile Marvels

Picture robots that can move and navigate on their own, exploring uncharted territories in space, performing search and rescue missions, or even delivering packages to our doorstep.

Human-Like Helpers

Envision robots that resemble humans, not just in appearance but also in their movements and interactions. These are the robots designed to understand and assist us in ways that feel natural.

AI-Powered Partners

Think of robots that aren’t just machines but intelligent partners. They learn from experience, adapt to different situations, and make decisions using cutting-edge artificial intelligence and machine learning.

Let’s wrap up our robotics research topics. As we have seen that there is endless innovation in robotics research topics. That is why there are lots of robotics research topics to explore.

As the technology is innovating everyday and continuously evolving there are lots of new challenges and discoveries are emerging in the world of robotics.

With these robotics research topics you would explore a lot about the future endeavors of robotics.  These topics would also tap on your creativity and embrace your knowledge about robotics. So let’s implement these topics and feel the difference.

Frequently Asked Questions

How can i get involved in robotics research.

To get started in robotics research, you can pursue a degree in robotics, computer science, or a related field. Join robotics clubs, attend conferences, and seek out research opportunities at universities or tech companies.

Are there any ethical concerns in robotics research?

Yes, ethical concerns in robotics research include issues related to job displacement, privacy, and the use of autonomous weapons. Researchers are actively addressing these concerns to ensure responsible development.

What are the career prospects in robotics research?

Robotics research offers a wide range of career opportunities, including robotics engineer, AI specialist, data scientist, and robotics consultant. The field is constantly evolving, creating new job prospects.

How can robotics benefit society?

Robotics can benefit society by improving healthcare, increasing manufacturing efficiency, conserving the environment, and aiding in disaster response. It has the potential to enhance various aspects of our lives.

What is the role of AI in robotics research?

AI plays a crucial role in robotics research by enabling robots to make intelligent decisions, adapt to changing environments, and perform complex tasks. AI and robotics are closely intertwined, driving innovation in both fields.

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Thesis topics for rss-supervised theses.

The Robotics and Semantic Systems group does research related to various areas of widely understood Artificial Intelligence, including Cognitive Robotics, Industrial Robotics and Automation, Human-Robot Interaction, Natural Language Processing, Machine Learning, Knowledge Representation. Most of our research has practical nature and consists of building systems with specific properties mimicking some aspects of intelligence.

The courses we offer can be found in the following page:  https://rss.cs.lth.se/education/courses/

If you are interested, please contact us to discuss further, either based on an existing thesis proposal (e.g., from industry) or on your ideas and interests. Either contact a person (listed below) based on your specific interests, or Jacek Malec, who is currently the RSS coordinator for thesis work. 

• Cognitive Robotics (prerequisite EDAP20), Elin Anna Topp, Volker Krueger, Jacek Malec, Maj Stenmark
• Construction Robotics (prerequisite FRTF20), Mathias Haage
• Human-Robot Interaction (prerequisite EDAP20 or EDAP01), Elin Anna Topp, Maj Stenmark
• Knowledge Representation (prerequisite EDAP01), Jacek Malec
• Machine Learning (prerequisite EDAN96 or equivalent), Pierre Nugues
• Natural Language Processing (prerequisite EDAN20), Pierre Nugues

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Automated Robot Design With Artificial Evolution

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Robots usually do a single job and would perform better when their mechanical structure is particularly designed for their designated task. However, manually designing robots with mechanical properties that implicitly compute and execute a specific task is very hard and time consuming. To assist designers, a platform that automatically designs dynamical mechanisms is needed. Evolutionary algorithms (EAs) are suggested as invention machines for similar domains and seem promising for automatic mechanism design. However key obstacles are the representation of dynamical mechanisms, operators for sexual reproduction and maintaining population diversity. We present a robot genome based on graph theory that is as compact as possible, has no genetic multiplicity and is completely closed. We define a sexual operator that is able to merge both topology and parameter data from two parent-robots to form a child-robot that is essentially a mixture of both parents. And we investigate which of the most commonly used methods for diversity maintenance improve optimization performance for dynamical mechanism design best. We demonstrate that our new representation and sexual operator enables automatic design of dynamical mechanisms. Specifically, we showcase automatic design of two-dimensional mechanisms (with a single degree of freedom) that track a straight line (Roberts mechanism) as well as an ellipse by virtue of their kinematic and dynamical properties. The best optimization results are obtained by maintaining diversity with a combination of the island and the diffusion model. We demonstrate the practical value of the evolved mechanisms by extracting design principles and by redesigning a mechanism to include mathematically hard-to-formulate objectives such as aesthetics and symmetry.

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Dissertations for Robotics Engineering

Bakke, christine k., perceptions of professional and educational skills learning opportunities made available through k-12 robotics programming, craighead, jeffrey david, improving ad-hoc team performance using video games, devaram, prashanth, e-quality: using dimensional index values towards improving classification accuracy, gorlewicz, jenna lynn, the efficacy of surface haptics and force feedback in education, howell, abraham l., development and validation of a low cost, flexible, open source robot for use as a teaching and research tool across the educational spectrum, laughlin, sara rose, robotics: assessing its role in improving mathematics skills for grades 4 to 5, mogenson, michael, the ar drone labview toolkit: a software framework for the control of low-cost quadrotor aerial robots, o'connell, brian, the development of the paperbots robotics kit for inexpensive robotics education activities for elementary students, palmer, jeremiah, application of a universal language for low-cost classroom robots, schweikardt, eric, designing modular robots, silk, eli michael, resources for learning robots: environments and framings connecting math in robotics, tse, susan b., mindstorms controls toolkit: hands-on, project-based learning of controls, whitehead, stephen h., relationship of robotic implementation on changes in middle school students' beliefs and interest toward science, technology, engineering and mathematics, sign in or register, sign in using email & password.

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101+ Simple Robotics Research Topics For Students

Imagine a world where machines come to life, performing tasks on their own or assisting humans with precision and efficiency. This captivating realm is the heart of robotics—a fusion of engineering, computer science, and technology. If you’re a student eager to dive into this mesmerizing field, you’re in for an electrifying journey. 

In this blog, we’ll unravel the secrets of robotics research, highlight its significance, and unveil an array of interesting robotics research topics. These topics are perfect for middle and high school students, making the exciting world of robotics accessible to all. Let’s embark on this adventure into the future of technology and innovation!

In your quest to explore robotics, don’t forget the valuable support of services like Engineering Assignment Help . Dive into these fascinating research topics and let us assist you on your educational journey

What is Robotics Research Topic?

Table of Contents

A robotics research topic is a specific area of study within the field of robotics that students can investigate to gain a deeper understanding of how robots work and how they can be applied to various real-world problems. These topics can range from designing and building robots to exploring the algorithms and software that control them.

Research topics in robotics can be categorized into various subfields, including:

• Mechanical Design: Studying how to design and build the physical structure of robots, including their components and materials.
• Sensors and Perception: Investigating how robots can sense and understand their environment through sensors like cameras, infrared sensors, and ultrasonic sensors.
• Control Systems: Exploring the algorithms and software that enable robots to move, make decisions, and interact with their surroundings.
• Human-Robot Interaction: Researching how robots can collaborate with humans, including topics like natural language processing and gesture recognition.
• Artificial Intelligence (AI): Studying how AI techniques can be applied to robotics, such as machine learning for object recognition and path planning.
• Applications: Focusing on specific applications of robotics, such as medical robotics, autonomous vehicles, and industrial automation.

Why is Robotics Research Important?

Before knowing robotics research topics, you need to know the reasons for the importance of robotics research. Robotics research is crucial for several reasons:

Advancing Technology

Research in robotics leads to the development of cutting-edge technologies that can improve our daily lives, enhance productivity, and solve complex problems.

Solving Real-World Problems

Robotics can be applied to address various challenges, such as environmental monitoring, disaster response, and healthcare assistance.

Inspiring Innovation

Engaging in robotics research encourages creativity and innovation among students, fostering a passion for STEM (Science, Technology, Engineering, and Mathematics) fields.

Educational Benefits

Researching robotics topics equips students with valuable skills in problem-solving, critical thinking, and teamwork.

Career Opportunities

A strong foundation in robotics can open doors to exciting career opportunities in fields like robotics engineering, AI, and automation.

Also Read: Quantitative Research Topics for STEM Students

Easy Robotics Research Topics For Middle School Students

Let’s explore some simple robotics research topics for middle school students:

Robot Design and Building

1. How to build a simple robot using household materials.

2. Designing a robot that can pick up and sort objects.

3. Building a robot that can follow a line autonomously.

4. Creating a robot that can draw pictures.

5. Designing a robot that can mimic animal movements.

6. Building a robot that can clean and organize a messy room.

7. Designing a robot that can water plants and monitor their health.

8. Creating a robot that can navigate through a maze of obstacles.

9. Building a robot that can imitate human gestures and movements.

10. Designing a robot that can assemble a simple puzzle.

11. Developing a robot that can assist in food preparation and cooking.

Robotics in Everyday Life

1. Exploring the use of robots in home automation.

2. Designing a robot that can assist people with disabilities.

3. How can robots help with chores and housekeeping?

4. Creating a robot pet for companionship.

5. Investigating the use of robots in education.

6. Exploring the use of robots for food delivery in restaurants.

7. Designing a robot that can help with grocery shopping.

8. Creating a robot for home security and surveillance.

9. Investigating the use of robots for waste recycling.

10. Designing a robot that can assist in organizing a bookshelf.

Robot Programming

1. Learning the basics of programming a robot.

2. How to program a robot to navigate a maze.

3. Teaching a robot to respond to voice commands.

4. Creating a robot that can dance to music.

5. Programming a robot to play simple games.

6. Teaching a robot to recognize and sort recyclable materials.

7. Programming a robot to create art and paintings.

8. Developing a robot that can give weather forecasts.

9. Creating a robot that can simulate weather conditions.

10. Designing a robot that can write and print messages or drawings.

Robotics and Nature

1. Studying how robots can mimic animal behavior.

2. Designing a robot that can pollinate flowers.

3. Investigating the use of robots in wildlife conservation.

4. Creating a robot that can mimic bird flight.

5. Exploring underwater robots for marine research.

6. Investigating the use of robots in studying insect behavior.

7. Designing a robot that can monitor and report air quality.

8. Creating a robot that can mimic the sound of various birds.

9. Studying how robots can help in reforestation efforts.

10. Investigating the use of robots in studying coral reefs and marine life.

Robotics and Space

1. How do robots assist astronauts in space exploration?

2. Designing a robot for exploring other planets.

3. Investigating the use of robots in space mining.

4. Creating a robot to assist in space station maintenance.

5. Studying the challenges of robot communication in space.

6. Designing a robot for collecting samples on other planets.

7. Creating a robot that can assist in assembling space telescopes.

8. Investigating the use of robots in space agriculture.

9. Designing a robot for space debris cleanup.

10. Studying the role of robots in exploring and mapping asteroids.

These robotics research topics offer even more exciting opportunities for middle school students to explore the world of robotics and develop their research skills.

Latest Robotics Research Topics For High School Students

Let’s get started with some robotics research topics for high school students:

Advanced Robot Design

1. Developing a robot with human-like facial expressions.

2. Designing a robot with advanced mobility for rough terrains.

3. Creating a robot with a soft, flexible body.

4. Investigating the use of drones in agriculture.

5. Developing a bio-inspired robot with insect-like capabilities.

6. Designing a robot with the ability to self-repair and adapt to damage.

7. Developing a robot with advanced tactile sensing for delicate tasks.

8. Creating a robot that can navigate both underwater and on land seamlessly.

9. Investigating the use of drones in disaster response and relief efforts.

10. Designing a robot inspired by cheetahs for high-speed locomotion.

11. Developing a robot that can assist in search and rescue missions in extreme weather conditions, such as hurricanes or wildfires.

Artificial Intelligence and Robotics

1. How can artificial intelligence enhance robot decision-making?

2. Creating a robot that can recognize and respond to emotions.

3. Investigating ethical concerns in AI-driven robotics.

4. Developing a robot that can learn from its mistakes.

5. Exploring the use of machine learning in robotic vision.

6. Exploring the role of AI-driven robots in space exploration and colonization.

7. Creating a robot that can understand and respond to human emotions in healthcare.

8. Investigating the ethical implications of autonomous vehicles in urban transportation.

9. Developing a robot that can analyze and predict weather patterns using AI.

10. Exploring the use of machine learning to enhance robotic prosthetics.

Human-Robot Interaction

1. Studying the impact of robots on human mental health.

2. Designing a robot that can assist in therapy sessions.

3. Investigating the use of robots in elderly care facilities.

4. Creating a robot that can act as a language tutor.

5. Developing a robot that can provide emotional support.

6. Studying the psychological impact of humanoid robots in educational settings.

7. Designing a robot that can assist individuals with neurodegenerative diseases.

8. Investigating the use of robots for mental health therapy and counseling.

9. Creating a robot that can help children with autism improve social skills.

10. Developing a robot companion for the elderly to combat loneliness.

Robotics and Industry

1. How are robots transforming the manufacturing industry?

2. Investigating the use of robots in 3D printing.

3. Designing robots for warehouse automation.

4. Developing robots for precision agriculture.

5. Studying the role of robotics in supply chain management.

6. Exploring the integration of robots in the construction and architecture industry.

7. Investigating the use of robots for recycling and waste management in cities.

8. Designing robots for autonomous maintenance and repair of industrial equipment.

9. Developing robotic solutions for monitoring and managing urban traffic.

10. Studying the role of robotics in the development of smart factories and Industry 4.0.

Cutting-Edge Robotics Applications

1. Exploring the use of swarm robotics for search and rescue missions.

2. Investigating the potential of exoskeletons for enhancing human capabilities.

3. Designing robots for autonomous underwater exploration.

4. Developing robots for minimally invasive surgery.

5. Studying the ethical implications of autonomous military robots.

6. Exploring the use of robotics in sustainable energy production.

7. Investigating the use of swarming robots for ecological conservation and monitoring.

8. Designing exoskeletons for individuals with mobility impairments for daily life.

9. Developing robots for autonomous planetary exploration beyond our solar system.

10. Studying the ethical and legal aspects of AI-powered military robots in warfare.

These robotics research topics offer high school students the opportunity to delve deeper into advanced robotics concepts and address some of the most challenging and impactful issues in the field.

Robotics research is a captivating field with a wide range of robotics research topics suitable for students of all ages. Whether you’re in middle school or high school, you can explore robot design, programming, AI integration , and cutting-edge applications. Robotics research not only fosters innovation but also prepares you for a future where robots will play an increasingly important role in various aspects of our lives. So, pick a topic that excites you, and embark on your journey into the fascinating world of robotics!

I hope you enjoyed this blog about robotics research topics for middle and high school students.

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Robotics Research Topics – 10 Free Topics With Example Outline By Experts

In the past few years, robotics has proliferated in educational institutions educating students from high school to PhD levels. For the benefit of the global technology market, students are very eager to work with a variety of dynamic and useful robots. Thus, to become more professional, ambitious techies hunt for some of the intriguing robotics research topics for their projects and dissertations. These robotics projects and dissertations help students develop a comprehensive understanding of the field. Let us look at some of the most popular research and thesis topics for robotics projects in 2022.

What Are The 10 Intriguing Robotics Research Topics?

1. vision and perception in robotics.

Vision and perception in robotics are the most popular issue in robotics research topics. Researchers are highly interested in understanding the vision and perception among robots. You can narrow down your focus to the computational and engineering domains that seek to comprehend perception and recognition among the robots. You can direct your research efforts to enhance the existing robotic systems and creation of new models for the autonomous functioning of robots. Your study will focus on using and comprehending the best ways to combine sensor data and enhance the sense perception among the robots.

2. Social Humanoid Robots

Shalu and Sophia have increased interest in humanoid robots in recent years. As a result, it is one of the new and popular themes for robotics research topics. The theme may concentrate on various functional goals, flaws, potential areas of development, skills, and many other things. Finding a topic for a robotics project using humanoid robots is quite easy.

3. Consciousness And Robots

Consciousness is one of the perplexing issues that vex researchers worldwide in the field of robotics. It is one of the most basic and ultimate philosophical questions that you can choose for your research among robotics research topics. There has been an increased debate among scientists and academics about the robots’ consciousness. Humans are conscious beings who are aware of their actions; however, the question remains if the robots can perform all the tasks performed by humans, is it possible to impart consciousness in them? You can evaluate the existing debates and literature on the issue and provide your insights on the issue that you think can address the research question comprehensively.

4. Healthcare Robots

Healthcare robots are one of the most popular robotics research topics for robotics dissertations since the healthcare industry has begun using robots for various tasks to safeguard and provide for patients. The goal of the robotics project can be to improve the capabilities of robots, create a robotic assistant, or pursue any other avenue for bridging human limits in the healthcare industry. You can discuss the possible implications and benefits of introducing healthcare robot assistants in society.

5. Economic Disparities And The Rise Of Robots

The world is divided between the “haves” and “have nots” pronounced by the famous philosopher Karl Marx. All history is a history of class struggle, and people who own the means of production dominate the top hierarchy, which is so prevalent in our modern societies. You can discuss the economic and structural inequalities that will determine who will have access to the robots. The economic aspect of robot technology is among the fascinating robotics research topics. You can explore the underpinning economic disparities and their role in shaping the robotics industry.

6. Robots In Agriculture

Students have many options when choosing thesis topics for robotics projects and agricultural robots for research. Farming robots are expected to revolutionise the global agriculture sector. With agro-robot projects, students can concentrate on these intriguing developments and applications. Robots are changing the entire structure of agriculture, thereby increasing food production on a massive scale.

7. Dependence On Robots

Every technology and innovation have merits and demerits, just like the atomic bomb, which is a powerful force for fulfilling energy demands; however, it is also a destructive force capable of destroying humanity. Similarly, robots are no exception to this. Indeed, the increased use of robots has improved the human condition and made operations and processes easier. You can see robots in every walk of life, from cleaning instruments to food-making appliances. However, there is also a downside, which is the increased dependence of humans on robots. You can choose to work on the disadvantages of increased reliance on robots among the robotics research topics. You can argue that the increased dependence of humans on robots is making them lazy.

8. Domestic Robots

Robots for the home can ease one’s life by providing intelligent features to cope with a hectic schedule. The need for domestic or household robots to keep the home in order, give company to a lone child, or take care of a pet is quite high. There are household robots that are proficient in many different fields, including security systems. Therefore, working on home robots is the most popular issue in the list of robotics research topics.

9. Nano Robots

One of the most popular research and thesis topics in robotics is working on a particular area of one of the many nanorobot domains. It might be related to health care, biology, cars, or any other industry worldwide. One of the newest and most popular robot technology sectors uses nanorobots, which have many types of parts. Many nanotechnology initiatives are still in the research and development phase.

10. Robotic Weapons

Developed and developing countries are highly interested in acquiring robotic technologies for their military arsenal. There is an increased interest and countries are spending significantly on research and development to enhance robotic technologies for combat and warfare. You can analyse and evaluate the ethical aspects of such practices in your robotics research. The proliferation of robotic warfare can be devastating for humanity and have serious repercussions for the security and peace of the entire world.

We have outlined the topmost intriguing robotics research topics for you. You can choose any one of the research topics for your dissertation or final project. Choosing a topic that aligns with your research interests and fascinates you is important. If you are facing any difficulty, then hire our experts by clicking here .

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Robotics projects for thesis.

Hello fellow Robotic Engineers!

Any robotic engineers able to advice or give more details on the different areas of robotics, mainly perception, localization, planning & control? I did some googling but couldn't quite grasp what exactly will be done in each of these pillars, in terms of work. What kind of work can one expect when delving into one of these pillars of robotics? A lot of maths, coding, statistics? What kind of literature would I need to know or look up as I work on a project based on this pillar of robotics?

Personally, I am more inclined towards software-based aspects of robotics (because I really like software & programming haha) but am not sure which area of robotics (from the few listed above) I would want to delve further into for my thesis project. I've included a link containing the few listed projects available that I could choose to work towards for my final year thesis. https://imgur.com/a/gZdzbmT

Just a bit of background about myself, I am decent at programming (took the OOP & Data Structures & Algorithms classes, got B+ for both), am currently halfway through Andre Ng's Coursera course on Machine Learning. Had school clubs experience doing some SolidWorks 3D modelling for a robotics club & humanitarian engineering (project innovation, concept ideation & mostly soft skills developed) for disabled people & internship experience as a Data Engineer intern, building ETL pipelines (in VBA, Python) (( also here i got to mess around with numpy & matplotlib )) & am actually currently interning as a software engineer intern at a bank!

The modules/classes i took throughout my mechanical engineering major so far has been pretty general (as per the planned curriculum of my major by the college) as compared to my peers in other colleges, where their mechatronics stream has been pretty streamlined & focused from Year 1 of their education!

Hence, my concern has been the lack of relevant knowledge & experience throughout my academic curriculum & whether that will affect me in terms of performing well in robotics related thesis projects? I am more than willing to put in the hard work but I also want to be realistic about my capabilities...

I hope any robotic engineers could help shed some light & possible give some advice on which one of the thesis projects might be best suited for me (based off my skills & abilities) (( should there be any other information I need to include that might help you guys understand me better, do let me know & I'd be more than happy to include it! ))

Thank you so much guys, I really appreciate any responses! :')

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The future of robotics

Guest Jeannette Bohg is an expert in robotics who says there is transformation happening in her field brought on by recent advances in large language models.

The LLMs have a certain common sense baked in and robots are using it to plan and to reason as never before. But they still lack low-level sensorimotor control – like the fine skill it takes to turn a doorknob. New models that do for robotic control what LLMs did for language could soon make such skills a reality, Bohg tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.

Listen on your favorite podcast platform:

Related : Jeannette Bohg , assistant professor of computer science

[00:00:00] Jeannette Bohg: Through things like ChatGPT, we have been able to do reasoning and planning on the high level, meaning kind of on the level of symbols, very well known in robotics, in a very different way that we could do before.

[00:00:17] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. If you enjoy The Future of Everything, please hit follow in whatever app you're listening to right now. This will guarantee that you never miss an episode. 

[00:00:29] Today, Professor Jeannette Bohg will tell us about robots and the status of robotic work. She'll tell us that ChatGPT is even useful for robots. And that there are huge challenges in getting reliable hardware so we can realize all of our robotic dreams. It's the future of robotics. 

[00:00:48] Before we get started, please remember to follow the show and ensure that you'll get alerted to all the new episodes so you'll never miss the future, and I love saying this, of anything.

[00:01:04] Many of us have been thinking about robots since we were little kids. When are we going to get those robots that can make our dinner, clean our house, drive us around, make life really easy? Well, it turns out that there's still some challenges and they're significant for getting robots to work. There are hardware challenges.

[00:01:20] It turns out that the human hand is way better than most robotic manipulators. In addition, robots break. They work in some situations like factories, but those are dangerous robots. They just go right through whatever's in front of them. 

[00:01:34] Well, Jeannette Bohg is a computer scientist at Stanford University and an expert on robotics. She's going to tell us that we are making good progress in building reliable hardware and in developing algorithms to help make robots do their thing. What's perhaps most surprising is even ChatGPT is helping the robotics community, even though it just does chats. 

[00:01:58] So Jeannette, there's been an increased awareness of AI in the last year, especially because of things like ChatGPT and what they call these large language models. But you work in robotics, you're building robots that sense and move around. Is that AI revolution for like chat, is that affecting your world? 

[00:02:15] Jeannette Bohg: Yeah. Um, yeah, very good question. It definitely does. Um, in, um, surprising ways, honestly. So I think for me, my research language has always been very interesting, but somewhat in the, you know, in the background from the kind of research I'm doing, which is like on robotic manipulation. And with the, um, with this rise of things like ChatGPT or large language models, suddenly, um, doors are being opened, uh, in robotics that were really pretty closed.

[00:02:46] Russ Altman: Metaphorical or physical or both? 

[00:02:48] Jeannette Bohg: Physically. That's exactly, that's a very good question because physically robots are very bad at open doors, but metaphorically speaking, these, uh, we can talk about that as well, metaphorically speaking through things like ChatGPT, we have been able to do reasoning and planning on the high level. Meaning kind of on the level of symbols, very well known in robotics in a very different way that we could do before. 

[00:03:12] So let's say, for example, you're in a kitchen and you want to make dinner. Um, and, uh, you know, there are so many steps that you have to do for that, right? And they don't have to do something, they don't necessarily have to do something with how you move your hands and all of this.

[00:03:27] It's really just like, I'm laying out the steps of what I'm going to do for making dinner. And this kind of reasoning is suddenly possible in a much more open-ended way, right? Because we can, uh, these language models, they have this common-sense knowledge kind of in baked in them. And now we can use them in robotics to do these task plans, right? That, um, that are really consisting of so many steps and they kind of make sense. It's not always correct. 

[00:03:55] Russ Altman: Right, right. 

[00:03:55] Jeannette Bohg: Um, I mean, if you try ChatGPT, you know, it's hallucinating thing. It's like making stuff up. Um, but, um, that's the challenge, uh, actually, and how to use these models in robotics. But the good thing is they open up these doors, metaphorically speaking again, um, to just do this task planning in an open-ended way. Um, and you know, and they can just like, um, they also allow to have this very natural interface between people and robots as well. That's another, 

[00:04:26] Russ Altman: Great. So that's really fascinating. So. If I understood your answer, you said that like for a high level, here's kind of the high-level script of how to make dinner, you know, get the dishes, get the ingredients. Um, do you find that there's a level of detail, I think implied in your answer, is that there's a level of detail that you need to get the robot to do the right things that it's not yet able to specify. 

[00:04:49] Are you optimistic that it will be able to do that? Or do you think it's going to be an entirely different approach to like, you know, move the manipulator arm to this position and grasp it gently? Do you think that that's going to be in the range of ChatGPT or will that be other algorithms? 

[00:05:03] Jeannette Bohg: Yeah. So I think to some extent, again, like these, you know, common sense, um, understanding of the world is in there. So for example, the idea that a glass could be fragile and you have to pick it up in a gentle way, or, uh, let's say you have to grasp a hammer by the handle or, you know, the tool tip of, uh, that tool is like over here or something like this.

[00:05:26] These are things that actually, um, help a robot to also plan its motion. Not just kind of this high-level task planning, but actually understand where to grasp things and maybe how much pressure to apply. Um, but they still, uh, they still cannot be directly generate an action, right? Like, so the action that a robot needs to compute is basically how do I move my hand? Like where exactly, like every millisecond, uh, or at least every ten milliseconds or something like that. And that is not what these models do. Um, and that's totally fine because to do that, they need completely different, they would need completely different training data that actually has this information in there.

[00:06:09] Um, like the actual motion of the robot arm needs to be given to these models in order to do that kind of prediction. Um, and so I think, um, so yeah, so that is actually the biggest, one of the biggest challenges in robotics to get to the same level of data that you have in areas like natural language processing or computer vision, that these, uh, models like ChatGPT, have consumed so far, right?

[00:06:38] So that, these models have been trained on trillions of tokens, right? Like multiple trillions of tokens. I don't know what the current maximum is. Um, but it's like, yeah, a lot. And in robotics, we have, uh, more like in the order of hundred thousands data of data points, hundred thousands. This is like millions of, um, it's a, by, uh, the difference is a factor of millions.

[00:07:06] Russ Altman: Now let me just ask you about that because I'm surprised you say that because I think about in many cases robots are trying to do things that we see in video by humans all the time. Like probably on television you could find many, many examples of somebody picking up a glass or opening a door, but it sounds to me like that's not enough for you. Like, in other words, these pictures of people doing things that doesn't turn into useful training data for the robot. And I guess that kind of makes sense. Although I'm a little surprised that we haven't figured out a way to take advantage of all of that human action to inform the video action. So talk to me a little bit about that. 

[00:07:43] Jeannette Bohg: Yeah, yeah. This is like a very interesting question. So the data that I said is too little right now, uh, in comparison to natural language processing and computer vision, that's really data that has been directly collected on the robot. 

[00:07:54] Russ Altman: Okay. So it's robot examples of them reaching, them touching.

[00:07:58] Jeannette Bohg: Yeah. And so that's like painstakingly collected with like joysticks and stuff like this, right? Like it's very tedious. That's why it's, I don't think possible to get to the same level of data, but you bring up a very good point, right? Like on YouTube. I mean, I'm watching YouTube all the time to just figure out like how to do something right?

[00:08:16] And how to repair something or do this and that, and yeah, we are learning from that and we are learning when we are growing up from our parents or whoever is like showing us how to do things. And, um, we want robots to do exactly the same. Uh, and that is like a super interesting research question. Uh, but the reason why it's a research question and not solved, um, is that in a video, um, you see a hand of a person, for example. But this hand, like our hand, sorry, I actually cut myself. 

[00:08:46] Russ Altman: Yes, I see that. For those who are listening, there's a little Band-Aid on Jeannette's hand. 

[00:08:51] Jeannette Bohg: But our hand is actually amazing, right? Like we have these five fingers, we have like, I don't know, it's even difficult to actually count how many degrees of freedom and joints our hand has, but it's like twenty-seven or something like that. It's soft, it can be very stiff, but it can also be very compliant. It's like, an amazing universal tool. And our robot hands are completely different. Unfortunately, I don't have one here, but basically, it's like, like a gripper. Very easy, very, um, very simple. Um, and it's because of that, it's very limited in what it can do. Um, and it might also need to do, um, things that a person does or tasks that a person does in a completely different way. 

[00:09:30] Russ Altman: I see, I see. 

[00:09:31] Jeannette Bohg: To, um, you know, to achieve the same task if it's even possible at all. And so if a robot looks at a video of a person, it needs to somehow understand like, okay, how does this map, uh, to my, my body right. Like my body only has two. 

[00:09:47] Russ Altman: Yeah, no, that's a really, so it's like, if somebody was watching Vladimir Horowitz play the piano, it's not very useful to show them a YouTube of Vladimir and say, just play it like that because he can do things that we can't do. 

[00:09:59] Jeannette Bohg: Right. That's exactly right. And I've heard that Rachmaninoff, for example, uh, has like these insane, had these insanely big hands and therefore, um, he could play, uh, his pieces. But they had like, uh, you're basically in order to play it, you had like to have a very specific difference between your thumb and your pinky, for example, like the distance, 

[00:10:20] Russ Altman: Span, the span of your, 

[00:10:21] Jeannette Bohg: Yeah. 

[00:10:21] Russ Altman: Okay. So that's a really good answer to my question is that the videos are relevant, but we, they're not dealing with beautiful human hands. And so there would have to be a translation of whatever was happening in the video to their world and it's and that would be difficult. 

[00:10:37] Jeannette Bohg: Yes, that is difficult. But people are looking into this, right? Like that's a super interesting research question on actually how. 

[00:10:43] Russ Altman: And because the positive the upside as we've talked about is that you would then have lots and lots of training data. If you could break that code of how to turn human actions in video into instructions for robot. Okay, that's extremely helpful.

[00:10:57] But I want to get to some of the technical details of your work because it's fascinating, but before we get there, another backup, background question is the goal for the robots. Are we trying to, I know you've written a lot about autonomous robots, but you've also talked about how robots can also work with humans to augment them.

[00:11:16] And I want to ask if those are points on a continuum. Like, it seems like autonomous would be different from augmenting a human, but maybe in your mind they work together. So how should we think about, and what should we expect the first generation or the second generation of robotic assistants to be like?

[00:11:34] Jeannette Bohg: Yeah, this is a very good question. So first of all, I would say, yes, uh, this is like, um, points on a spectrum, right? There are solutions, uh, on a spectrum from, uh, teleoperation, I would say, where you basically puppeteer a robot to do something that's typically done for data collection. Um, or, uh, you know, the, on the other end of the spectrum, you have this fully autonomous, it's basically a humanoid that we see in movies. Right. 

[00:11:59] Russ Altman: That's like the vacuum cleaner in my living room, my Roomba. 

[00:12:02] Jeannette Bohg: Right, right. Exactly. Yeah. That one is definitely autonomous. 

[00:12:05] Russ Altman: It seems fully autonomous to me. I have no idea when it's going to go on or off or where it's going to go. 

[00:12:12] Jeannette Bohg: Yeah. Nobody knows. Nobody knows. 

[00:12:15] Russ Altman: Forgive me. Forgive me. 

[00:12:16] Jeannette Bohg: You bought it. I also had one once, uh, back in the days and you know, I just turn it on and then I left because I knew it would take hours and hours to do what it needed to do. Um, 

[00:12:26] Russ Altman: I'm sorry, that was a little bit of a distraction. But yeah, tell me about the, this, um, spectrum. 

[00:12:31] Jeannette Bohg: Yeah. So I think there are ways in which, um, robots can really augment people in that, uh, they can, for example, um, they, uh, theoretically, they could have more strength, right? Like, so, uh, um, that there are lots of people who, it's not my area, but there are lots of people who built these exoskeletons or prosthetic devices, which I actually also find really interesting. They're typically very lightweight, uh, have an easy interface. Um, so that's interesting, but they can also kind of support people who have to lift heavy things, for example. So I think that's one way on how you can think about augmentation of people to help them. Another one is maybe still autonomous, but it's still augmenting people in a way.

[00:13:15] So one example I want to bring up, this is a shout out to, uh, Andrea Thomaz and Vivian Chu who are like, um, leading this, um, startup called Diligent Robotics and I recently heard a keynote from her at a conference. And I thought they did something really smart, which is they went first into hospitals, uh, to observe what nurses are doing all the, all day, right?

[00:13:37] Like, what are they doing with their hours? And to their surprise, what nurses really spend a lot of time on was just like shuttling around supplies between different places instead of actually taking care of patients, right? Which is what they're like trained to do and really good at, why are we using them to shuttle stuff around?

[00:13:55] And so what they decided is like, oh, we actually don't need a robot to do the patient care or do the stocking or whatever. What we actually need is a robot that just shuttled stuff around in a hospital, uh, where it still needs a hand to actually push elevator buttons and push door buttons and things like that. Or like maybe opening a door again, right? Um, like we had in the beginning. And I thought like, oh, this is such a great augmentation if you want, right? Like that. The nurses can actually now spend time on what they're really good at and what they're needed for and what they're trained for, which is patient care, and just stop worrying about where the supplies are, where things like blood samples or things have to go.

[00:14:36] Russ Altman: And it sounds like it might also create a, I don't know, I'm not going to say anything is easy, but a slightly more straightforward engineering challenge to start. 

[00:14:45] Jeannette Bohg: Right. So I think we're so far away from general purpose robots, right? Like we, I, I don't know how long it's going to take, but it's still going to take a lot of time. And I think a smart way to bring robotics into our everyday world is to actually, uh, ideas like the ones from Diligent Robotics, where you really go and analyze what people quote unquote waste their time on. It's not really a waste of time, of course. But you know, it could be done in like a, in an automated way actually, um, to give people time for the things they're actually really good at and where robots are still very bad at.

[00:15:18] Um, yeah. So I think, um, we will probably see, hopefully see more of this, right? Like in the future, like very small things. You can think of Roomba, for example, doing something kind of very small and I don't know how good it is, like it's good enough, 

[00:15:37] Russ Altman: Compared to ignoring our floors, which was our strategy for the first twenty-five years, this is a huge improvement. Because now, even if it's not a perfect sweep, it's more sweeping than we would do under normal circumstances. 

[00:15:49] Jeannette Bohg: Yeah, I agree with that. So I think like these small ideas, right, like that are not again, like this general purpose robot. But, uh, like some very, uh, smart ideas about where robots can help people with things that they find really annoying, um, and are doable for current robotic technology. I think that's what we will see in the next a few years. Um, and again, like it's a, they are still autonomous again, but they are augmenting people in this way. 

[00:16:16] Russ Altman: Right. That resolves that what I thought was attention, but you just explained why it's not really attention. This is the future of everything with Russ Altman. More with Jeannette Bohg next.

[00:16:41] Welcome back to The Future of Everything. I'm Russ Altman, your host, and I'm speaking with Professor Jeannette Bohg from Stanford University. 

[00:16:47] In the last segment, we went over some of the challenges of autonomous versus augmenting robots. We talked a little bit about the data problems. And in this next segment, we're going to talk about hardware. What are robots going to look like? How are they going to work? How expensive are they going to be? I want to get into kind of a fun topic, which is the hardware. You made a brief mention of the hands, uh, and how amazing human hands are, but the current robotic hands, uh, they're not quite human yet.

[00:17:14] Um, where are we with hardware and what are the challenges and what are some of the exciting new developments? 

[00:17:20] Jeannette Bohg: Yeah. Uh, hardware is hard. It's one thing that I've been told is a saying in Silicon Valley recently. But yeah, uh, I think hardware and robotics is one of the biggest challenges. And I think we have very good hardware when it comes to automation in, uh, places like factories that are, um, you know, building cars and all of this. And it's very reliable, right? And that's what you want. But when it comes to the kinds of platforms that we want to see at some point in our homes or in hospitals, again, um, these platforms have to be equally robust and durable and repeatable and all of this. Uh, but we're not there. We're not there. Like literally, uh, I'm constantly, uh, talking to my students and they're constantly repairing whatever else, whatever new things broken again with our robots. I mean, it's constant. Um,

[00:18:12] Russ Altman: But it's interesting to know, just interrupt you. But the guys at Ford Motor Company and the big industry, they have figured out, is it a question of budget? Is it a question that they just spend a lot of money on these robots or are they too simple compared to what you need? I just want to explore a little bit why those industrial ones are so good. 

[00:18:30] Jeannette Bohg: Yeah, so that is a very good question. I think they are, um, first of all, they are still very expensive, uh, robots actually. So they still cost like, uh, multiple ten thousands of dollars. Um, but yeah, they are also, they have a very, they follow a very specific methodology, which is they have to be, um, very stiff, uh, meaning that not like our arms, uh, which are kind of naturally kind of, um, squishy and give in to any kind of things we may be bumping in. Uh, these robots are not, right? Like they're going to go no matter what, to a specific point you sent them to. And, um, that is just the way they are built. And maybe that's also why they are so robust, uh, as well. Um, but they are dangerous, right? 

[00:19:15] Russ Altman: Yes. 

[00:19:15] Jeannette Bohg: So that's why they're in cages. And, uh, people can't get close to them. Uh, and that's of course not what we want in the real world. So the kinds of robots that we work in the research world with are more geared towards like, oh, when can we bring them into someone's home, uh, or have them at least work alongside a person in warehouses or things like that. Um, and so these, this technology I think is just not quite as mature and as robust. Um, and also not produced in that, at that, um, you know, there are just not so many copies of those as there are of these industrial robots. And I think they're just not as optimized yet. 

[00:19:53] Russ Altman: So when you said the robots cost tens of thousands of dollars, are those the robots you're using? 

[00:19:58] Jeannette Bohg: Uh, yeah. 

[00:19:59] Russ Altman: That your students are fixing all day?

[00:20:01] Jeannette Bohg: Yes, unfortunately, this is exactly right. Like I spent so much money from my lab on, on buying forty thousand dollar robot arms, um, or seventy thousand dollar robot arms, right? Like that's the kind of, uh, money we need to spend to have these research platforms that we need to show our results and test our results. And, um, actually, um, yeah. So for example, um, one of the projects we have, um, is, uh, a mobile manipulator. Uh, so it's, uh, a robot arm on top of a mobile platform. Think of a Roomba with an arm, maybe just like way more expensive. It's more like, 

[00:20:36] Russ Altman: A forty thousand dollar Roomba. I gotcha. 

[00:20:39] Jeannette Bohg: At least. Yeah. So, um, think about that. And that project was really fun. It's like, uh, using this mobile manipulator to clean up your house. So it's, um, uh, it's basically talking to you to figure out like, oh, what are your preferences? Where are your dirty socks going? Where are your, you know, Coke cans, your empty Coke cans going? Um, and then it, uh, kind of from your few examples, compresses that down to like some general categories of where to put stuff.

[00:21:05] And so that's the robot we, uh, we did a project on, and people are very excited about it. They loved it. It's even throwing stuff into bins. It's like a basketball star in a way. Um, and people really love it. And also researcher loves it. Researchers loved it, because there's this mobile base. Uh, so the, basically the, um, you know, the thing on wheels, basically, 

[00:21:29] Russ Altman: Yeah, it can move around. 

[00:21:30] Jeannette Bohg: Um, that one, uh, is very unique. It was a donation from some company. Um, and it's, uh, it has like specific capabilities, but it's like three of a kind exist in the world and, um, we, and people can't buy it and it's very disappointing. So, um, but again, yeah, these are the arms that we are constantly, uh, constantly like repairing and it's like scary even because if we lose this platform, we can't do our research. 

[00:21:58] Russ Altman: Right.

[00:21:58] Jeannette Bohg: So one of the things I'm doing for the first time in my lab, actually, and again, I'm a computer scientist, not a mechanical engineer. But, uh, with one of my students, we're looking at how to develop a low-cost version of this, uh, mobile base that has like these special abilities and is very maneuverable.

[00:22:17] Um, and I'm, my hope is that with this platform first, I hope it's reliable, but if not, at least you can like cheaply repair it, um, and can get in there, right? Like even if you're a student with, who is a computer scientist, not a mechanical engineer, and I hope that it just allows you to buy many of these platforms rather than just one, uh, you know, that you have to baby around all the time, but you can maybe hopefully buy many of them, you will hopefully open source all of this design.

[00:22:47] And then, uh, my, what I'm really excited about is to use this low-cost platform, um, to do maybe swarm based manipulation of many robots, uh, collaborating with each other. 

[00:23:00] Russ Altman: So in your current view, what would be the basic functionality of one of these units or is that flexible? But is it a hand? Is it two hands? Is it, uh, is it mobile like a Roomba? 

[00:23:12] Jeannette Bohg: Yeah, it's basically, uh, um, yeah, you could think of it as a Roomba plus plus basically, which has an arm. So it's not just like, uh, vacuum your floor, but it's actually putting things away. Right? Like if you, uh, for those who have children, right, like I, I think they are always most excited about, about this, what we call TidyBot, um, because it's just like putting things into the right places instead of you stepping on these Lego pieces in the middle of the night, right.

[00:23:39] So that's what you, um, that's what we're going for. Uh, and it would be one mobile base with one arm and one hand. And then let's say you have multiple of them. So, uh, for, you could, for example, think of when you have to move, right? Like I personally think moving to another place is, I mean, it's the worst, right?

[00:23:59] Russ Altman: Packing, packing and unpacking is the worst. 

[00:24:01] Jeannette Bohg: Packing, unpacking, but also like carrying stuff around. So imagine if you have like this fleet of robots, right? That just helps you getting the sofa through like these tight spaces and all of this. So that's kind, 

[00:24:11] Russ Altman: Paint a picture for me in this version one-point-oh, how tall is it? Are we talking two feet tall or five feet tall? How big is it? 

[00:24:19] Jeannette Bohg: Now you're getting me with the feets and the inches. 

[00:24:22] Russ Altman: I'm sorry. You can draw meters, whatever, whatever works. 

[00:24:25] Jeannette Bohg: Okay. Yeah. So actually, uh, so the base is actually fairly, uh, low. Um, and actually pretty heavy so that it has like a low center of mass. It's probably like, I guess a foot tall. Um, I let's say twenty centimeters. 

[00:24:39] Russ Altman: Yeah. 

[00:24:39] Jeannette Bohg: Um, and then the arm, if it's fully stretched out and just pointing up, it is probably like one and a half meters long on top of that. 

[00:24:48] Russ Altman: That's five feet or so. 

[00:24:50] Jeannette Bohg: Really like fully stretched out, which it usually isn't to do stuff. It's like, 

[00:24:54] Russ Altman: But then it could reach things on tables. That's the, that's what I was trying to get to. It could reach tables. It could maybe reach into the dryer or the washing machine or stuff like that. It might be within range. 

[00:25:05] Jeannette Bohg: Uh, all of this then, uh, also just making your bed. Uh,

[00:25:09] Russ Altman: Yeah, I hate that. 

[00:25:11] Jeannette Bohg: Yeah, terrible. 

[00:25:11] Russ Altman: So let me ask, uh, since we're talking about what it looks like. Um, in so much of the sci fi, robots seem to have to look like humans. What's your take on that? Like, is it important that the robot, is it, maybe it's not, maybe it's important that it not look like a human, where are you in this whole humanoid debate? 

[00:25:29] Jeannette Bohg: Okay, this is a very good question. And I'm probably going to say something contentious, uh, or maybe not, I don't know. But yeah, I think building a humanoid robot is really exciting from a research standpoint. Um, and I think it's just looks cool. So it gives you like these super cool demos that you see from all these startups right now, 

[00:25:49] Russ Altman: Right, right.

[00:25:49] Jeannette Bohg: On Twitter and all. I mean, this looks very cool. I just personally, um, don't think that it's like the most, um, like economical way maybe to think, uh, about like, what's the most useful robot. I think the arguments are typically like, oh, but, um, the spaces that we walk in and work in and live in, they're all designed for people. So why not making a robot platform that is having the same form factor and can squeeze through tight places and use all the tools and all of that. It kind of makes sense to me.

[00:26:25] Um, but again, like coming back to my earlier point, right? Where I'm thinking like general purpose robots are really, really far away. Um, and I think the, um, like narrow, like the, uh, let's say closer future, not the future of everything, but the future in like the next few years. Uh, it's maybe, um, it's maybe going to look more at like very specific purpose robots that are maybe on wheels because that's just easier, right? Like you don't have to worry about this. Um, and they can do relatively specialized things in like one environment, like going to a grocery store and doing restocking, um, or things like that. Right? Um, 

[00:27:04] Russ Altman: I've also heard that you have to be careful about making it humanoid because then humans might impute to it human capabilities, human emotions. And by having it look like a weird device, it reminds you that indeed this is a device and maybe the user interaction might be more natural and less misled because you start, you know, you don't treat it like it's a human and that might not be the goal. In other cases, like for care of the elderly, maybe you want it to look humanoid because it might be more natural. But okay, that's a very, very helpful answer. 

[00:27:37] Jeannette Bohg: Yeah, I think this is a very good point, actually, that people probably attribute much more intelligence, uh, whatever, whatever way we want to define that to a humanoid robot rather than to something like TidyBot that we had, right? Which is just one arm. It really looks very robot, I have to say. 

[00:27:55] Russ Altman: So what is the outlook to finish up in the last minute or so? Where are we with this platform? And when are you going to start shipping? 

[00:28:04] Jeannette Bohg: We published this on Twitter basically. There were lots of people like how much money, like when can I buy this? And, uh, and yeah, again, like it's, we're pretty far away from having like a robot that we can just literally, uh, give you and then it's gonna work, right? 

[00:28:19] Like, I think there's so much engineering. I think you can probably bring it up like similar to autonomous driving, right? Like fairly, maybe easily to ninety percent, but then the rest of it is all these corner cases, right? That you have to deal with and it's going to be really hard. So I don't want to make a prediction of when we're going to have this. Again, I think it's going to be more like more special purpose, uh, robots. Um, again, maybe a Roomba is maybe not so far away with an arm, right? 

[00:28:48] Russ Altman: I love it. I love it. And I know that in the academic world, ninety percent and cheap will lead to a lot of innovation. 

[00:28:56] Jeannette Bohg: Right. That's the other point, like, when is it affordable, right? Like nobody's going to buy a robot that is as much as a luxury car, right? 

[00:29:04] Russ Altman: Right. 

[00:29:05] Jeannette Bohg: That can't even do anything really well. 

[00:29:07] Russ Altman: Right. 

[00:29:08] Thanks to Jeannette Bohg. That was The Future of Robotics. 

[00:29:11] Thanks for tuning into this episode too. With more than 250 episodes in our archives, you have instant access to a whole range of fascinating conversations with me and other people. If you're enjoying the show, please remember to consider sharing it with friends, family, and colleagues. Personal recommendations are the best way to spread the news about The Future of Everything. You can connect with me on X or Twitter, @RBAltman. And you can connect with Stanford Engineering @StanfordENG.

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Assistant professor of computer science, robotics.

Description

The Department of Computer Science invites applications for a full-time tenure-track position at the rank of Assistant Professor, in the areas of Robotics. Core technical contributions could come from multiple areas, including but not limited to, cyber-physical systems, embodied artificial intelligence, edge intelligence, reinforcement learning, human-robot interaction, multimodal sensing, and/or sensor fusion. Applicants should have a track record of publications in CS/robotics-related fields.

The Computer Science department is home to 24 tenure-track faculty members and is committed to growing that number by 50% over the next decade. The department is housed in the recently opened  Class of 1982 Engineering and Computer Science Center . Our curriculum includes strong Ph.D. and M.S. programs and outstanding undergraduate majors. Graduate students and postdoctoral scholars are supported by the   Guarini School for Graduate and Advanced Studies , including their   diversity and inclusion initiatives . We are especially interested in applicants who have a demonstrated ability to contribute to Dartmouth's diversity initiatives in STEM research, such as the   Women in Science Program ,   E. E. Just STEM Scholars Program ,  Wright Center for the Study of Computation and Just Communities , and   Academic Summer Undergraduate Research Experience .

Dartmouth  is committed to academic excellence and encourages the open exchange of ideas within a culture of mutual respect. People with different backgrounds, life experiences, and perspectives make the Dartmouth community diverse, which enhances academic excellence. Applicants should include a statement that addresses how their research, teaching, service, and/or life experiences prepare them to advance Dartmouth's commitment to diversity in service of academic excellence.

Qualifications

Applicants must have a Ph.D. in Computer Science or a closely related field, or be All But Dissertation (ABD) with a degree conferred by the start of the appointment. Effective classroom teaching is essential for this position.

Application Instructions

Please submit all materials electronically via Interfolio . Letters may be addressed to the search committee chair, Professor Alberto Quattrini Li.

• Statement of research experience and plans (up to 5 pages);
• Statement of teaching experience and plans (up to 5 pages);
• Statement of how the applicant's research, teaching, service, and/or life experiences prepare them to advance Dartmouth's commitment to diversity in service of academic excellence (up 5 to pages);
• Four (4) letters of recommendation, at least one of which should comment on teaching.

Review of applications will begin on  December 1, 2024  and continue until the position is filled.

For questions regarding this position, please contact the search chair, Professor Alberto Quattrini Li:  [email protected] .  

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Not just another band from Boston

He didn’t expect to do well at MIT; he didn’t expect his music to be successful. But engineer Tom Scholz ’69, SM ’70, became an inventor, producer, and philanthropist—and the artistic and technical brains behind a juggernaut rock band.

• Elisabeth C. Rosenberg archive page

In 1976, Tom Scholz ’69, SM ’70, was a 29-year-old product design engineer working at Polaroid on audio electronics and tape-recording technology, with 11 patents under his belt. But few colleagues knew what Scholz did after hours, why he often came in late, or why he was, in his own words, “a horrible employee.” 

For five years, Scholz had been painstakingly crafting music and lyrics, and perfecting phenomenally complex sound production, in the makeshift basement recording studio at his apartment in Watertown, Massachusetts—playing all his own instruments and mixing them on an analog 12-track recorder until they sounded as natural as a band that had played together for years. He got a friend, local musician Brad Delp, to record the lead vocals, mixing that in too. After finally picking up a contract with CBS’s Epic Records, he recruited some more friends from the Boston music scene to be the “faces” for an album with the working title Boston . When it came time to choose a name for the band, someone at the studio suggested using the same one. Having grown up in Toledo tuning in to Boston’s WBZ radio at night to hear British rock bands, Scholz readily agreed. 

“Honestly, I thought the recording I made in 1976 was going to be forgotten by the beginning of 1977,” he says. He was just hoping to get the song on the radio so he could get gigs in local clubs with a song people recognized. “I actually didn’t realize there was anything serious happening with my album until I was working in my back room [at Polaroid]—I had a secret back room, sort of like a boiler room, in the bowels of the building in Tech Square—and somebody comes running and says, ‘Hey! Your song’s playing in the drafting department!’” he recalls. He raced off to hear it but only caught the tail end. And that kept happening until finally, several months after “More Than a Feeling” hit the Top 10, he heard it all the way through on the radio.Still, Scholz didn’t quit his day job until Boston became a national arena headliner. 

Donald Thomas Scholz, a teenage fan of model airplanes, junker cars, basketball, and classical music, matriculated at MIT in 1965 to study mechanical engineering. Competition was brutal, he understood: “When I showed up for my freshman orientation, they sat us all in a large assembly area and put up a chart showing everyone’s SAT scores … I thought, ‘Now I’m in trouble.’” Scholz was so sure he’d flunk out that by the end of his first semester he’d already applied to transfer—but then he discovered he’d gotten a 4.8 average and decided to stay. In fact, he performed so well that MIT offered him a scholarship for a one-year master’s in mechanical engineering. His thesis project—a pair of simple A-frame hoists that made it possible to assemble prefabricated homes without a crane—led to his first patent, in 1972.

“The things I was exposed to at MIT were the basis for absolutely everything,” he says. “I use things I learned at MIT in the engineering department every day of my life—numerous times, every day.” MIT’s encouragement of blue-sky thinking would also stick with him. “It made me a little less fearful about looking like a fool when I tried new things because some of them aren’t gonna work,” he says. “I learned how to learn when I went to MIT, and I tried not to stop.”

Scholz didn’t pick up a guitar until he was 21, after getting hooked on bands like the Kinks and the Yardbirds. He dove into learning to play and soon became fascinated by what analog processors and amplifiers could do to the sound. As an MIT junior, he used an electric piano to compose an instrumental piece that eventually became the song “Foreplay” on Boston’s debut album. At the time, he was living in a fourth-floor Allston apartment: “I had had enough [understanding] of dynamics and so forth to understand how sound can transfer through a wood floor. The three nurses that lived below us were extremely patient with me because I usually wrote between 12 and two in the morning, and every time I pounded on those keys they felt it through the ceiling—and never complained. I think they felt sorry for me because I had to go to MIT.”

“Somehow I had to make those two things coexist—you know, being a positive influence and making some awesome music that people would think was kick-ass rock and roll.”

In the six years between his master’s and the release of Boston , Scholz built and deployed increasingly complex homebrew gadgets to create the otherworldly music he heard in his head. His favorite musical device was what he named the “hyperspace pedal.” “You can play a note with vibrato forever,” is how he describes its effect. “You can make a chord go up and down by several octaves. More importantly, you can make sounds that NASA would be scared of from a rocket ship. And I used that to my heart’s delight recording all of the Boston albums.” 

At Polaroid, Scholz’s primary responsibility was creating audio tape for the Polavision instant video system. Although that ended up being sold without audio, his work was instrumental in helping him develop the musical devices that gave Boston its singular sound. During that period, Scholz says, Polaroid “was almost like an extension of MIT—it had the same sort of mindset,” which gave him the freedom to build and experiment. Nearly all his Polaroid patents involved audio recording and reproduction.

In 1977, Scholz repaired the 16x4 channel studio mixer he’d used to lay down all the instrument tracks for Boston’s debut album. A year later, he mixed their second album on an Auditronics 501 26x4 console.

Scholz’s nights were for producing music. As a neophyte but increasingly adept guitarist, he began renting time at expensive recording studios. When recording between midnight and 8:00 a.m. above a bar an hour’s drive from home became untenable, Scholz decided to build his “really awful but workable” basement studio in Watertown (where, at 6 foot 5, he had to duck to avoid hitting his head on the way down). “Without saying too much to the landlord … I built a couple of temporary walls, and used an awful lot of carpeting and sound-absorbing materials that I could scrounge up,” he remembers. “Because this was done on, of course, an extremely low budget. And I managed to keep the noise level down enough that I could record.”

That volume control took some of his MIT-nurtured ingenuity. Scholz had to record his multilayered guitar tracks “at very high amplifier output” to get the sound he wanted. “A hundred watts through the speakers that worked with that amp were just incredibly loud—not something you could use in the basement of a house, not even in a house, because people down the street and in the neighborhood would be complaining about it. I had to find a way to decrease the output of the amp without changing the sound appreciably.” To this end, two of his personal patents—for “Constant Volume Distortion Control” and the physical unit in which the distortion control was housed—became the basis for what he called the Power Soak attenuator, the first product sold by his company, Scholz Research & Design. 

Executives at Epic Records were not enthusiastic about marketing a record that had been produced and recorded in a basement. Scholz and his “just another band out of Boston” flew to LA to record the vocals. Then Scholz went home to Watertown and, at the Epic producer’s request, re-recorded most of the album—“in exactly the same place as the demo they didn’t want to use, with exactly the same equipment, as close as I could to the original performance,” he says. “And that’s what they decided was great, and ready to be released.” Boston went on to become one of the best-selling debut albums of all time. 

Boston last toured in 2017 and has sold more than 31 million records worldwide. Today, Scholz’s home studio lies fallow—not for lack of inspiration, but because it remains resolutely analog, and “unfortunately there’s almost no one left locally who can maintain or repair analog studio equipment.”

But he is still busy and engaged. He and his wife, Kim, operate the DTS Charitable Foundation, which he founded in 1987 to promote a “vegetarian lifestyle, and prevention of cruelty and suffering to animals both nonhuman and human” (he has been a vegetarian for decades). Knee injuries sidelined him from basketball a few years ago, but he does freestyle figure skating and plays “extreme croquet,” which is typically played on challenging terrain without the usual out-of-bounds rules. He has a pilot’s license, and one of his current passions is designing high-performance radio-­controlled airplanes. “I love it,” he says. He is mourning the loss of the “scary-fast red delta-wing airplane” that he built in 1972, flew for 52 years, and considers his favorite invention: “Unfortunately, it had an in-flight breakup earlier this year and was destroyed. I was quite crushed by that. So was the airplane, by the way.”

Scholz says he and Kim have slowly turned their house into a workshop and lab. “There is no ‘house,’” he says. “When we have someone coming over for dinner, we actually have to clear out space to have a table that we can all sit at together.” (A proclivity for making things runs in the family; his son, Jeremy Scholz ’05, majored in mechanical engineering at MIT.) Scholz does interviews in “what used to be the electronics area for troubleshooting and fixing all this stuff in my studio,” he says. “It’s become a drafting area and a radio-controlled-aircraft fabrication/assembly area, and I have a small shop in what was the furnace room.”

He still hopes to get his studio back up and running, “because I am still writing music, believe it or not, in what’s left of my brain,” he says. “And it’s very frustrating not to be able to go in and make the recording of what I hear.”

Scholz marvels that classical composers could hear everything in their heads. “You listen to Vivaldi or Bach, and you think, ‘How did he know that those violins were going to work together when they all came together at the same time?’ He could only play one,” he says. “Whereas I always had to record things, listen to them together, and then go back and … ‘Well, that was the wrong bass line! I’ll try a different one,’ and so on.”

He initially came up with this method of layering different recordings together to please himself. “When I first started doing this, I was a kid in my 20s—well, late 20s—and I was just trying to put some music down that I thought sounded good. I actually didn’t believe that anyone else would think it sounded great,” he says. When it took off commercially, he felt compelled to become a positive role model as well. “Somehow I had to make those two things coexist—you know, being a positive influence and making some awesome music that people would think was kick-ass rock and roll.” 

“After the first album, I was suddenly placed in a position where I was a figure that people were going to emulate. Kids listened to this music,” he says. “I felt this enormous weight, that everything that I did and everything that I said and anything I put on an album was going to have a possible effect on someone.” 

While other rockers were cultivating wild personas, he focused on the connection between self-improvement, higher education, and Boston’s music and tried “to encourage people to do things that I thought were a good step for mankind,” he says. “So when someone 50 years later comes and says, ‘Oh, this song really helped me get through,’ it means the world to me.”

Scholz has always been true to himself and to his music, even in the days when he was being rejected by one record label after another. “Having failed miserably,” he says, “I thought, ‘You know what? I’m going to make one more demo, and it’s going to be just exactly the way I see it, and the way I want to hear it, and I’m going to play every single part.’ And that worked, oddly enough. It’s been a wild ride.” 

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