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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.03315 |
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| _version_ | 1866909500787654656 |
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| author | D'Ambrosio, David B. Abelian, Jonathan Abeyruwan, Saminda Ahn, Michael Bewley, Alex Boyd, Justin Choromanski, Krzysztof Cortes, Omar Coumans, Erwin Ding, Tianli Gao, Wenbo Graesser, Laura Iscen, Atil Jaitly, Navdeep Jain, Deepali Kangaspunta, Juhana Kataoka, Satoshi Kouretas, Gus Kuang, Yuheng Lazic, Nevena Lynch, Corey Mahjourian, Reza Moore, Sherry Q. Nguyen, Thinh Oslund, Ken Reed, Barney J Reymann, Krista Sanketi, Pannag R. Shankar, Anish Sermanet, Pierre Sindhwani, Vikas Singh, Avi Vanhoucke, Vincent Vesom, Grace Xu, Peng |
| author_facet | D'Ambrosio, David B. Abelian, Jonathan Abeyruwan, Saminda Ahn, Michael Bewley, Alex Boyd, Justin Choromanski, Krzysztof Cortes, Omar Coumans, Erwin Ding, Tianli Gao, Wenbo Graesser, Laura Iscen, Atil Jaitly, Navdeep Jain, Deepali Kangaspunta, Juhana Kataoka, Satoshi Kouretas, Gus Kuang, Yuheng Lazic, Nevena Lynch, Corey Mahjourian, Reza Moore, Sherry Q. Nguyen, Thinh Oslund, Ken Reed, Barney J Reymann, Krista Sanketi, Pannag R. Shankar, Anish Sermanet, Pierre Sindhwani, Vikas Singh, Avi Vanhoucke, Vincent Vesom, Grace Xu, Peng |
| contents | We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_03315 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Robotic Table Tennis: A Case Study into a High Speed Learning System D'Ambrosio, David B. Abelian, Jonathan Abeyruwan, Saminda Ahn, Michael Bewley, Alex Boyd, Justin Choromanski, Krzysztof Cortes, Omar Coumans, Erwin Ding, Tianli Gao, Wenbo Graesser, Laura Iscen, Atil Jaitly, Navdeep Jain, Deepali Kangaspunta, Juhana Kataoka, Satoshi Kouretas, Gus Kuang, Yuheng Lazic, Nevena Lynch, Corey Mahjourian, Reza Moore, Sherry Q. Nguyen, Thinh Oslund, Ken Reed, Barney J Reymann, Krista Sanketi, Pannag R. Shankar, Anish Sermanet, Pierre Sindhwani, Vikas Singh, Avi Vanhoucke, Vincent Vesom, Grace Xu, Peng Robotics Machine Learning We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0. |
| title | Robotic Table Tennis: A Case Study into a High Speed Learning System |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2309.03315 |