The tremendous success of behavior cloning (BC) in robotic manipulation has been largely confined to tasks where demonstrations can be effectively collected through human teleoperation. However, demonstrations for contact-rich manipulation tasks that require complex coordination of multiple contacts are difficult to collect due to the limitations of current teleoperation interfaces. We investigate how to leverage model-based planning and optimization to generate training data for contact-rich dexterous manipulation tasks. Our analysis reveals that popular sampling-based planners like rapidly exploring random tree (RRT), while efficient for motion planning, produce demonstrations with unfavorably high entropy. This motivates modifications to our data generation pipeline that prioritizes demonstration consistency while maintaining solution diversity. Combined with a diffusion-based goal-conditioned BC approach, our method enables effective policy learning and zero-shot transfer to hardware for two challenging contact-rich manipulation tasks.

翻译：行为克隆在机器人操作领域的巨大成功主要局限于那些能够通过人类遥操作有效收集演示的任务。然而，对于需要多接触点复杂协调的接触密集型操作任务，由于当前遥操作界面的限制，演示数据难以收集。本文研究了如何利用基于模型的规划与优化来为接触密集型灵巧操作任务生成训练数据。我们的分析表明，诸如快速探索随机树等流行的基于采样的规划器，虽然在运动规划中效率很高，但产生的演示数据具有不利的高熵特性。这促使我们对数据生成流程进行改进，优先保证演示的一致性，同时维持解决方案的多样性。结合基于扩散的目标条件行为克隆方法，我们的方法能够实现有效的策略学习，并在两种具有挑战性的接触密集型操作任务上实现零样本硬件迁移。