Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills

• URL: http://arxiv.org/abs/2510.25634v1
• Date: Wed, 29 Oct 2025 15:39:53 GMT
• Title: Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills
• Abstract summary: We introduce a hierarchical framework that frames this challenge as an integrated skill planning & scheduling problem.<n>Our approach is built upon a library of single-arm and bimanual primitive skills, each trained using Reinforcement Learning (RL) in GPU-accelerated simulation.<n>We demonstrate that our method achieves higher success rates on complex, contact-rich tasks than end-to-end RL approaches.
• Score: 5.745006056037742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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