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.

翻译：长时程、高接触的双手操作任务提出了重大挑战，需要双臂之间混合并行执行与顺序协作的复杂协调。本文提出一种分层框架，将这一挑战构建为一个集成的技能规划与调度问题，超越了纯顺序决策，支持技能的同时调用。我们的方法建立在一个单臂与双手基础技能库之上，每个技能均在GPU加速的仿真环境中使用强化学习（RL）进行训练。随后，我们在技能组合数据集上训练一个基于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，我们的方法在复杂、高接触的任务上比端到端RL方法取得了更高的成功率，并且比传统的纯顺序规划器产生了更高效、协调的行为。