学习计划与调度的强化学习双臂机器人技能
Learning to Plan & Schedule with Reinforcement-Learned Bimanual Robot Skills
AI总结：
本文提出了一种基于强化学习的双臂机器人技能计划与调度框架，通过分层结构提升复杂任务的执行效率与协调性。
摘要：
长周期接触丰富的双臂操作提出了重大挑战，需要复杂的协调，涉及平行执行和臂间顺序协作的混合。在本文中，我们引入了一个分层框架，将这一挑战视为一个集成的技能计划与调度问题，超越纯粹的顺序决策，以支持同时调用技能。我们的方法基于一个单臂和双臂原始技能的库，每个技能均通过在GPU加速模拟中使用强化学习（RL）进行训练。然后，我们在技能组合的数据集上训练一个基于Transformer的规划器，作为高层次调度器，同时预测技能的离散调度及其连续参数。我们证明了我们的方法在复杂、接触丰富的任务上比端到端RL方法具有更高的成功率，并且比传统仅顺序的规划器产生更高效、协调的行为。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
