Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910012591308800 |
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| author | Wan, Weikang Ramos, Fabio Yang, Xuning Garrett, Caelan |
| author_facet | Wan, Weikang Ramos, Fabio Yang, Xuning Garrett, Caelan |
| contents | Long-horizon contact-rich bimanual manipulation presents a significant challenge, requiring complex coordination involving a mixture of parallel execution and sequential collaboration between arms. In this paper, we introduce a hierarchical framework that frames this challenge as an integrated skill planning & scheduling problem, going beyond purely sequential decision-making to support simultaneous skill invocation. Our approach is built upon a library of single-arm and bimanual primitive skills, each trained using Reinforcement Learning (RL) in GPU-accelerated simulation. We then train a Transformer-based planner on a dataset of skill compositions to act as a high-level scheduler, simultaneously predicting the discrete schedule of skills as well as their continuous parameters. We demonstrate that our method achieves higher success rates on complex, contact-rich tasks than end-to-end RL approaches and produces more efficient, coordinated behaviors than traditional sequential-only planners. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills Wan, Weikang Ramos, Fabio Yang, Xuning Garrett, Caelan Robotics Artificial Intelligence Long-horizon contact-rich bimanual manipulation presents a significant challenge, requiring complex coordination involving a mixture of parallel execution and sequential collaboration between arms. In this paper, we introduce a hierarchical framework that frames this challenge as an integrated skill planning & scheduling problem, going beyond purely sequential decision-making to support simultaneous skill invocation. Our approach is built upon a library of single-arm and bimanual primitive skills, each trained using Reinforcement Learning (RL) in GPU-accelerated simulation. We then train a Transformer-based planner on a dataset of skill compositions to act as a high-level scheduler, simultaneously predicting the discrete schedule of skills as well as their continuous parameters. We demonstrate that our method achieves higher success rates on complex, contact-rich tasks than end-to-end RL approaches and produces more efficient, coordinated behaviors than traditional sequential-only planners. |
| title | Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2510.25634 |