用强化学习训练双手技能，实现复杂操作的高效协同规划。
Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills
- 构建双臂基础技能库，通过强化学习在仿真中训练
- 用Transformer模型同时预测技能序列和参数，提升成功率
- 适合需要双手协调的长时序复杂操作任务
长时序高接触力双臂操作面临巨大挑战，需在双臂间实现并行执行与顺序协作的复杂协调。本文提出一种分层框架，将该问题建模为统一的技能规划与调度问题，突破纯顺序决策限制，支持技能的并行调用。方法基于单臂与双臂原始技能库，每个技能均通过强化学习在GPU加速的仿真环境中训练。随后，在技能组合数据集上训练基于Transformer的规划器，作为高层调度器，同时预测技能的离散调度序列及其连续参数。实验表明，该方法在复杂高接触任务上的成功率达92%，显著高于端到端强化学习方法，并生成比传统顺序规划更高效、协调的行为。
原文摘要 · Abstract (English)
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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