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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.03906 |
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| _version_ | 1866909597865869312 |
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| author | D'Ambrosio, David B. Abeyruwan, Saminda Graesser, Laura Iscen, Atil Amor, Heni Ben Bewley, Alex Reed, Barney J. Reymann, Krista Takayama, Leila Tassa, Yuval Choromanski, Krzysztof Coumans, Erwin Jain, Deepali Jaitly, Navdeep Jaques, Natasha Kataoka, Satoshi Kuang, Yuheng Lazic, Nevena Mahjourian, Reza Moore, Sherry Oslund, Kenneth Shankar, Anish Sindhwani, Vikas Vanhoucke, Vincent Vesom, Grace Xu, Peng Sanketi, Pannag R. |
| author_facet | D'Ambrosio, David B. Abeyruwan, Saminda Graesser, Laura Iscen, Atil Amor, Heni Ben Bewley, Alex Reed, Barney J. Reymann, Krista Takayama, Leila Tassa, Yuval Choromanski, Krzysztof Coumans, Erwin Jain, Deepali Jaitly, Navdeep Jaques, Natasha Kataoka, Satoshi Kuang, Yuheng Lazic, Nevena Mahjourian, Reza Moore, Sherry Oslund, Kenneth Shankar, Anish Sindhwani, Vikas Vanhoucke, Vincent Vesom, Grace Xu, Peng Sanketi, Pannag R. |
| contents | Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table tennis is a physically demanding sport which requires human players to undergo years of training to achieve an advanced level of proficiency. In this paper, we contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their detailed skill descriptors which model the agent's capabilities and help to bridge the sim-to-real gap and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real including an iterative approach to defining the task distribution that is grounded in the real-world and defines an automatic curriculum, and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45% (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100% matches vs. beginners and 55% matches vs. intermediate players, demonstrating solidly amateur human-level performance. Videos of the matches can be viewed at https://sites.google.com/view/competitive-robot-table-tennis |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03906 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Achieving Human Level Competitive Robot Table Tennis D'Ambrosio, David B. Abeyruwan, Saminda Graesser, Laura Iscen, Atil Amor, Heni Ben Bewley, Alex Reed, Barney J. Reymann, Krista Takayama, Leila Tassa, Yuval Choromanski, Krzysztof Coumans, Erwin Jain, Deepali Jaitly, Navdeep Jaques, Natasha Kataoka, Satoshi Kuang, Yuheng Lazic, Nevena Mahjourian, Reza Moore, Sherry Oslund, Kenneth Shankar, Anish Sindhwani, Vikas Vanhoucke, Vincent Vesom, Grace Xu, Peng Sanketi, Pannag R. Robotics Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table tennis is a physically demanding sport which requires human players to undergo years of training to achieve an advanced level of proficiency. In this paper, we contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their detailed skill descriptors which model the agent's capabilities and help to bridge the sim-to-real gap and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real including an iterative approach to defining the task distribution that is grounded in the real-world and defines an automatic curriculum, and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45% (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100% matches vs. beginners and 55% matches vs. intermediate players, demonstrating solidly amateur human-level performance. Videos of the matches can be viewed at https://sites.google.com/view/competitive-robot-table-tennis |
| title | Achieving Human Level Competitive Robot Table Tennis |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.03906 |