IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality – RSS 2023
Tags: Deep reinforcement learning, Learning from demonstrations
Algorithms, systems, and tools that solve assembly tasks in simulation with reinforcement learning (RL) and successfully achieve policy transfer to a real-world robotic assembly system.
Deformable Manipulation from Demonstrations (DMfD)
Tags: Deformable manipulation, Learning from demonstrations
A novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as inputs, given expert demonstrations.
Selective Object Rearrangement in Clutter – CoRL 2022
Tags: Deformable manipulation, Learning from demonstrations
An image-based, learned method for selective tabletop object rearrangement in clutter using a parallel jaw gripper.
Decentralized Control of Quadrotor Swarms with End to end Deep Reinforcement Learning (Simulation)
Tags: Deep reinforcement learning, Learning from demonstrations
Decentralized Control of Quadrotor Swarms with End to end Deep Reinforcement Learning (Physical)
Tags: Deep reinforcement learning, Learning from demonstrations
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors
Tags: Deep reinforcement learning, Learning from demonstrations, Meta learning
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. We use reinforcement learning to train policies in a simulation that transfer remarkably well to multiple different physical quadrotors. Our policies are low-level, i.e., we map the rotorcrafts’ state directly to the motor outputs. The trained control policies are very robust to external disturbances and can withstand harsh initial conditions such as throws. To the best of our knowledge, this is the first work that demonstrates that a simple neural network can learn a robust stabilizing low-level quadrotor controller without the use of a stabilizing PD controller; as well as the first work that analyses the transfer capability of a single policy to multiple quadrotors.
Publications
IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality.
Tang, Bingjie; Lin, Michael A.; Akinola, Iretiayo; Handa, Ankur; Sukhatme, Gaurav S.; Ramos, Fabio; Fox, Dieter; Narang, Yashraj S.: IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality. Bekris, Kostas E.; Hauser, Kris; Herbert, Sylvia L.; Yu, Jingjin (Ed.): Robotics: Science and Systems XIX, Daegu, Republic of Korea, July 10-14, 2023, 2023
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Alexa Arena: A User-Centric Interactive Platform for Embodied AI.
Gao, Qiaozi; Thattai, Govind; Gao, Xiaofeng; Shakiah, Suhaila; Pansare, Shreyas; Sharma, Vasu; Sukhatme, Gaurav S.; Shi, Hangjie; Yang, Bofei; Zheng, Desheng; Hu, Lucy; Arumugam, Karthika; Hu, Shui; Wen, Matthew; Guthy, Dinakar; Chung, Cadence; Khanna, Rohan; Ipek, Osman; Ball, Leslie; Bland, Kate; Rocker, Heather; Rao, Yadunandana; Johnston, Michael; Ghanadan, Reza; Mandal, Arindam; Hakkani-Tþr, Dilek; Natarajan, Prem: Alexa Arena: A User-Centric Interactive Platform for Embodied AI. CoRR, vol. abs/2303.01586, 2023
Learning Robot Manipulation from Cross-Morphology Demonstration
Salhotra, Gautam; Liu, I-Chun Arthur; Sukhatme, Gaurav S.: Learning Robot Manipulation from Cross-Morphology Demonstration Conference on Robot Learning (CoRL) 2023, 2023
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning.
Batra, Sumeet; Tjanaka, Bryon; Fontaine, Matthew C.; Petrenko, Aleksei; Nikolaidis, Stefanos; Sukhatme, Gaurav S.: Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning. Submitted to Neurips 2023, 2023
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QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control.
Huang, Zhehui; Batra, Sumeet; Chen, Tao; Krupani, Rahul; Kumar, Tushar; Molchanov, Artem; Petrenko, Aleksei; Preiss, James A.; Yang, Zhaojing; Sukhatme, Gaurav S.: QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control. CoRR, vol. abs/2306.09537, 2023
Tracking Fast Trajectories with a Deformable Object using a Learned Model.
Preiss, James A.; Millard, David; Yao, Tao; Sukhatme, Gaurav S.: Tracking Fast Trajectories with a Deformable Object using a Learned Model. 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022, pp. 1351-1357, IEEE, 2022
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation.
Heiden, Eric; Denniston, Christopher E.; Millard, David; Ramos, Fabio; Sukhatme, Gaurav S.: Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation. 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022, pp. 3638-3645, IEEE, 2022
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DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following.
Gao, Xiaofeng; Gao, Qiaozi; Gong, Ran; Lin, Kaixiang; Thattai, Govind; Sukhatme, Gaurav S.: DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following. IEEE Robotics Autom. Lett., vol. 7, no. 4, pp. 10049-10056, 2022
Learning Deformable Object Manipulation From Expert Demonstrations.
Salhotra, Gautam; Liu, I-Chun Arthur; Dominguez-Kuhne, Marcus; Sukhatme, Gaurav S.: Learning Deformable Object Manipulation From Expert Demonstrations. IEEE Robotics Autom. Lett., vol. 7, no. 4, pp. 8775-8782, 2022
Inferring Articulated Rigid Body Dynamics from RGBD Video.
Heiden, Eric; Liu, Ziang; Vineet, Vibhav; Coumans, Erwin; Sukhatme, Gaurav S.: Inferring Articulated Rigid Body Dynamics from RGBD Video. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022, Kyoto, Japan, October 23-27, 2022, pp. 8383-8390, IEEE, 2022
Supervised learning and reinforcement learning of feedback models for reactive behaviors: Tactile feedback testbed.
Sutanto, Giovanni; Rombach, Katharina; Chebotar, Yevgen; Su, Zhe; Schaal, Stefan; Sukhatme, Gaurav S.; Meier, Franziska: Supervised learning and reinforcement learning of feedback models for reactive behaviors: Tactile feedback testbed. Int. J. Robotics Res., vol. 41, no. 13-14, pp. 1121-1145, 2022
Selective Object Rearrangement in Clutter.
Tang, Bingjie; Sukhatme, Gaurav S.: Selective Object Rearrangement in Clutter. Liu, Karen; Kulic, Dana; Ichnowski, Jeffrey (Ed.): Conference on Robot Learning, CoRL 2022, 14-18 December 2022, Auckland, New Zealand, pp. 1001-1010, PMLR, 2022
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Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning.
Zentner, K. R.; Julian, Ryan; Puri, Ujjwal; Zhang, Yulun; Sukhatme, Gaurav S.: Towards Exploiting Geometry and Time for Fast Off-Distribution Adaptation in Multi-Task Robot Learning. CoRR, vol. abs/2106.13237, 2021
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning.
Batra, Sumeet; Huang, Zhehui; Petrenko, Aleksei; Kumar, Tushar; Molchanov, Artem; Sukhatme, Gaurav S.: Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning. CoRR, vol. abs/2109.07735, 2021
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation.
Heiden, Eric; Denniston, Christopher E.; Millard, David; Ramos, Fabio; Sukhatme, Gaurav S.: Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation. CoRR, vol. abs/2109.08815, 2021
A Simple Approach to Continual Learning by Transferring Skill Parameters.
Zentner, K. R.; Julian, Ryan; Puri, Ujjwal; Zhang, Yulun; Sukhatme, Gaurav S.: A Simple Approach to Continual Learning by Transferring Skill Parameters. CoRR, vol. abs/2110.10255, 2021
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence.
Roy, Nicholas; Posner, Ingmar; Barfoot, Tim D.; Beaudoin, Philippe; Bengio, Yoshua; Bohg, Jeannette; Brock, Oliver; Depatie, Isabelle; Fox, Dieter; Koditschek, Daniel E.; Lozano-PØrez, TomÆs; Mansinghka, Vikash; Pal, Christopher J.; Richards, Blake A.; Sadigh, Dorsa; Schaal, Stefan; Sukhatme, Gaurav S.; ThØrien, Denis; Toussaint, Marc; Panne, Michiel: From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence. CoRR, vol. abs/2110.15245, 2021
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges.
Zhao, Yizhou; Lin, Kaixiang; Jia, Zhiwei; Gao, Qiaozi; Thattai, Govind; Thomason, Jesse; Sukhatme, Gaurav S.: LUMINOUS: Indoor Scene Generation for Embodied AI Challenges. CoRR, vol. abs/2111.05527, 2021
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning.
Batra, Sumeet; Huang, Zhehui; Petrenko, Aleksei; Kumar, Tushar; Molchanov, Artem; Sukhatme, Gaurav S.: Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning. Faust, Aleksandra; Hsu, David; Neumann, Gerhard (Ed.): Conference on Robot Learning, 8-11 November 2021, London, UK, pp. 576-586, PMLR, 2021
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation.
Liu, I-Chun Arthur; Uppal, Shagun; Sukhatme, Gaurav S.; Lim, Joseph J.; Englert, Peter; Lee, Youngwoon: Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation. Faust, Aleksandra; Hsu, David; Neumann, Gerhard (Ed.): Conference on Robot Learning, 8-11 November 2021, London, UK, pp. 641-650, PMLR, 2021
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NeuralSim: Augmenting Differentiable Simulators with Neural Networks.
Heiden, Eric; Millard, David; Coumans, Erwin; Sheng, Yizhou; Sukhatme, Gaurav S.: NeuralSim: Augmenting Differentiable Simulators with Neural Networks. IEEE International Conference on Robotics and Automation, ICRA 2021, Xi'an, China, May 30 - June 5, 2021, pp. 9474-9481, IEEE, 2021
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Encoding Physical Constraints in Differentiable Newton-Euler Algorithm.
Sutanto, Giovanni; Wang, Austin S.; Lin, Yixin; Mukadam, Mustafa; Sukhatme, Gaurav S.; Rai, Akshara; Meier, Franziska: Encoding Physical Constraints in Differentiable Newton-Euler Algorithm. Bayen, Alexandre M.; Jadbabaie, Ali; Pappas, George J.; Parrilo, Pablo A.; Recht, Benjamin; Tomlin, Claire J.; Zeilinger, Melanie N. (Ed.): Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020, Online Event, Berkeley, CA, USA, 11-12 June 2020, pp. 804-813, PMLR, 2020
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics.
Millard, David; Heiden, Eric; Agrawal, Shubham; Sukhatme, Gaurav S.: Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics. CoRR, vol. abs/2001.08539, 2020
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm.
Sutanto, Giovanni; Wang, Austin S.; Lin, Yixin; Mukadam, Mustafa; Sukhatme, Gaurav S.; Rai, Akshara; Meier, Franziska: Encoding Physical Constraints in Differentiable Newton-Euler Algorithm. CoRR, vol. abs/2001.08861, 2020
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation.
Julian, Ryan; Swanson, Benjamin; Sukhatme, Gaurav S.; Levine, Sergey; Finn, Chelsea; Hausman, Karol: Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation. CoRR, vol. abs/2004.10190, 2020
Plan-Space State Embeddings for Improved Reinforcement Learning.
Pflueger, Max; Sukhatme, Gaurav S.: Plan-Space State Embeddings for Improved Reinforcement Learning. CoRR, vol. abs/2004.14567, 2020
Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed.
Sutanto, Giovanni; Rombach, Katharina; Chebotar, Yevgen; Su, Zhe; Schaal, Stefan; Sukhatme, Gaurav S.; Meier, Franziska: Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed. CoRR, vol. abs/2007.00450, 2020
NeuralSim: Augmenting Differentiable Simulators with Neural Networks.
Heiden, Eric; Millard, David; Coumans, Erwin; Sheng, Yizhou; Sukhatme, Gaurav S.: NeuralSim: Augmenting Differentiable Simulators with Neural Networks. CoRR, vol. abs/2011.04217, 2020
Scaling simulation-to-real transfer by learning a latent space of robot skills.
Julian, Ryan; Heiden, Eric; He, Zhanpeng; Zhang, Hejia; Schaal, Stefan; Lim, Joseph J.; Sukhatme, Gaurav S.; Hausman, Karol: Scaling simulation-to-real transfer by learning a latent space of robot skills. Int. J. Robotics Res., vol. 39, no. 10-11, 2020
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning.
Julian, Ryan; Swanson, Benjamin; Sukhatme, Gaurav S.; Levine, Sergey; Finn, Chelsea; Hausman, Karol: Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning. Kober, Jens; Ramos, Fabio; Tomlin, Claire J. (Ed.): 4th Conference on Robot Learning, CoRL 2020, 16-18 November 2020, Virtual Event / Cambridge, MA, USA, pp. 2120-2136, PMLR, 2020
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning.
Petrenko, Aleksei; Huang, Zhehui; Kumar, Tushar; Sukhatme, Gaurav S.; Koltun, Vladlen: Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, pp. 7652-7662, PMLR, 2020
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors.
Molchanov, Artem; Chen, Tao; Hünig, Wolfgang; Preiss, James A.; Ayanian, Nora; Sukhatme, Gaurav S.: Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, Macau, SAR, China, November 3-8, 2019, pp. 59-66, IEEE, 2019
Learning Manipulation Graphs from Demonstrations Using Multimodal Sensory Signals.
Su, Zhe; Kroemer, Oliver; Loeb, Gerald E.; Sukhatme, Gaurav S.; Schaal, Stefan: Learning Manipulation Graphs from Demonstrations Using Multimodal Sensory Signals. 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, May 21-25, 2018, pp. 2758-2765, IEEE, 2018
Region Growing Curriculum Generation for Reinforcement Learning.
Molchanov, Artem; Hausman, Karol; Birchfield, Stan; Sukhatme, Gaurav S.: Region Growing Curriculum Generation for Reinforcement Learning. CoRR, vol. abs/1807.01425, 2018
Auto-conditioned Recurrent Mixture Density Networks for Complex Trajectory Generation.
Zhang, Hejia; Heiden, Eric; Julian, Ryan; He, Zhangpeng; Lim, Joseph J.; Sukhatme, Gaurav S.: Auto-conditioned Recurrent Mixture Density Networks for Complex Trajectory Generation. CoRR, vol. abs/1810.00146, 2018
Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations.
He, Zhanpeng; Julian, Ryan; Heiden, Eric; Zhang, Hejia; Schaal, Stefan; Lim, Joseph J.; Sukhatme, Gaurav S.; Hausman, Karol: Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations. CoRR, vol. abs/1810.02422, 2018
Scaling Simulation-to-Real Transfer by Learning Composable Robot Skills.
Julian, Ryan; Heiden, Eric; He, Zhanpeng; Zhang, Hejia; Schaal, Stefan; Lim, Joseph J.; Sukhatme, Gaurav S.; Hausman, Karol: Scaling Simulation-to-Real Transfer by Learning Composable Robot Skills. Xiao, Jing; Krüger, Torsten; Khatib, Oussama (Ed.): Proceedings of the 2018 International Symposium on Experimental Robotics, ISER 2018, Buenos Aires, Argentina, November 5-8, 2018, pp. 267-279, Springer, 2018
Regrasping Using Tactile Perception and Supervised Policy Learning.
Chebotar, Yevgen; Hausman, Karol; Kroemer, Oliver; Sukhatme, Gaurav S.; Schaal, Stefan: Regrasping Using Tactile Perception and Supervised Policy Learning. 2017 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 27-29, 2017, AAAI Press, 2017
Feature selection for learning versatile manipulation skills based on observed and desired trajectories.
Kroemer, Oliver; Sukhatme, Gaurav S.: Feature selection for learning versatile manipulation skills based on observed and desired trajectories. 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, Singapore, May 29 - June 3, 2017, pp. 4713-4720, IEEE, 2017
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets.
Hausman, Karol; Chebotar, Yevgen; Schaal, Stefan; Sukhatme, Gaurav S.; Lim, Joseph J.: Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets. Guyon, Isabelle; Luxburg, Ulrike; Bengio, Samy; Wallach, Hanna M.; Fergus, Rob; Vishwanathan, S. V. N.; Garnett, Roman (Ed.): Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp. 1235-1245, 2017
Learning spatial preconditions of manipulation skills using random forests.
Kroemer, Oliver; Sukhatme, Gaurav S.: Learning spatial preconditions of manipulation skills using random forests. 16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016, Cancun, Mexico, November 15-17, 2016, pp. 676-683, IEEE, 2016
Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning.
Chebotar, Yevgen; Hausman, Karol; Su, Zhe; Sukhatme, Gaurav S.; Schaal, Stefan: Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, South Korea, October 9-14, 2016, pp. 1960-1966, IEEE, 2016
Online trajectory optimization to improve object recognition.
Potthast, Christian; Sukhatme, Gaurav S.: Online trajectory optimization to improve object recognition. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, South Korea, October 9-14, 2016, pp. 4765-4772, IEEE, 2016
Learning Relevant Features for Manipulation Skills using Meta-Level Priors.
Kroemer, Oliver; Sukhatme, Gaurav S.: Learning Relevant Features for Manipulation Skills using Meta-Level Priors. CoRR, vol. abs/1605.04439, 2016
Meta-level Priors for Learning Manipulation Skills with Sparse Features.
Kroemer, Oliver; Sukhatme, Gaurav S.: Meta-level Priors for Learning Manipulation Skills with Sparse Features. Kulic, Dana; Nakamura, Yoshihiko; Khatib, Oussama; Venture, Gentiane (Ed.): International Symposium on Experimental Robotics, ISER 2016, Tokyo, Japan, October 3-6, 2016, pp. 211-222, Springer, 2016
Generalizing Regrasping with Supervised Policy Learning.
Chebotar, Yevgen; Hausman, Karol; Kroemer, Oliver; Sukhatme, Gaurav S.; Schaal, Stefan: Generalizing Regrasping with Supervised Policy Learning. Kulic, Dana; Nakamura, Yoshihiko; Khatib, Oussama; Venture, Gentiane (Ed.): International Symposium on Experimental Robotics, ISER 2016, Tokyo, Japan, October 3-6, 2016, pp. 622-632, Springer, 2016
Learning to Switch Between Sensorimotor Primitives Using Multimodal Haptic Signals.
Su, Zhe; Kroemer, Oliver; Loeb, Gerald E.; Sukhatme, Gaurav S.; Schaal, Stefan: Learning to Switch Between Sensorimotor Primitives Using Multimodal Haptic Signals. Tuci, Elio; Giagkos, Alexandros; Wilson, Myra S.; Hallam, John (Ed.): From Animals to Animats 14 - 14th International Conference on Simulation of Adaptive Behavior, SAB 2016, Aberystwyth, UK, August 23-26, 2016, Proceedings, pp. 170-182, Springer, 2016
Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor.
Su, Zhe; Hausman, Karol; Chebotar, Yevgen; Molchanov, Artem; Loeb, Gerald E.; Sukhatme, Gaurav S.; Schaal, Stefan: Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor. 15th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2015, Seoul, South Korea, November 3-5, 2015, pp. 297-303, IEEE, 2015
Learning task error models for manipulation.
Pastor, Peter; Kalakrishnan, Mrinal; Binney, Jonathan; Kelly, Jonathan; Righetti, Ludovic; Sukhatme, Gaurav S.; Schaal, Stefan: Learning task error models for manipulation. 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 6-10, 2013, pp. 2612-2618, IEEE, 2013
Adaptive spatio-temporal organization in groups of robots.
Dahl, Torbjłrn S.; Mataric, Maja J.; Sukhatme, Gaurav S.: Adaptive spatio-temporal organization in groups of robots. IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, September 30 - October 4, 2002, pp. 1044-1049, IEEE, 2002
A testbed for Mars precision landing experiments by emulating spacecraft dynamics on a model helicopter.
Saripalli, Srikanth; Sukhatme, Gaurav S.: A testbed for Mars precision landing experiments by emulating spacecraft dynamics on a model helicopter. IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, September 30 - October 4, 2002, pp. 2097-2102, IEEE, 2002
On the Development of EMG Control for a Prosthesis Using a Robotic Hand.
Iberall, Thea; Sukhatme, Gaurav S.; Beattie, Denise; Bekey, George A.: On the Development of EMG Control for a Prosthesis Using a Robotic Hand. Proceedings of the 1994 International Conference on Robotics and Automation, San Diego, CA, USA, May 1994, pp. 1753-1758, IEEE Computer Society, 1994
