我们是否应该从基于采样的规划器中学习富接触操作策略？
Should We Learn Contact-Rich Manipulation Policies from Sampling-Based Planners?
AI总结：
针对富接触操作示教数据收集困难的问题，提出改进基于采样的规划器数据生成流程，结合基于扩散模型的目标条件BC方法，实现了有效的策略学习与硬件零样本迁移。
摘要：
行为克隆（BC）在机器人操作领域的巨大成功，很大程度上局限于那些可以通过人类遥操作有效收集示教数据的任务。然而，由于当前遥操作界面的局限性，需要多个接触点复杂协调的富接触操作任务的示教数据很难收集。我们研究了如何利用基于模型的规划与优化来为富接触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（RRT）等流行的基于采样的规划器，虽然在运动规划上很高效，但生成的示教数据具有不利的高熵。这促使我们对数据生成流程进行修改，在保持解的多样性的同时，优先考虑示教数据的一致性。结合基于扩散模型的目标条件BC方法，我们的方法实现了有效的策略学习，并在两项具有挑战性的富接触操作任务中实现了向硬件的零样本迁移。
英文摘要：
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.
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。
