用规划生成高质量演示数据，让机器人学会复杂触觉操作。
Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners?
- 用采样式规划器生成演示数据，优化一致性与多样性平衡
- 在两个接触密集任务上实现零样本硬件迁移
- 适合需要高精度触觉交互的机器人学习场景
行为克隆（BC）在机器人操作中取得显著成功，但主要局限于可通过人工遥操作有效收集示范的任务。对于需要多接触复杂协调的接触密集型操作任务，现有遥操作接口难以获取足够示范。本文研究如何利用基于模型的规划与优化生成训练数据。分析发现，主流采样式规划器如快速探索随机树（RRT）虽高效，但生成的示范熵过高，不利于学习。为此，我们改进数据生成流程，优先保证示范一致性，同时维持解的多样性。结合扩散模型驱动的目标条件化行为克隆方法，该方案在两个具有挑战性的接触密集型操作任务中实现了有效的策略学习，并成功实现零样本硬件迁移。
原文摘要 · Abstract (English)
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.
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