基于精确标注人类演示的模仿学习
Imitation Learning with Precisely Labeled Human Demonstrations
AI总结：
针对现有利用人类演示的方法在精确动作推断等方面的挑战，本研究通过为手持夹具分配独特易分割颜色，结合RANSAC和ICP配准方法实现精确末端执行器位姿估计，提升了策略性能。
摘要：
在模仿学习范式中，训练通用机器人需要大规模数据集，而这类数据集只能通过多样化的整理获得。由于相对易于收集，人类演示在适当整合时成为宝贵的补充。然而，现有利用人类演示的方法在推断精确动作、改善具身差距以及与前沿通用机器人训练流水线融合方面面临挑战。在本研究中，我们基于先前证明使用手持夹具高效收集数据可行性的研究，利用用户对夹具外观的控制——具体而言是为其分配独特且易于分割的颜色——从而能够简单可靠地应用RANSAC和ICP配准方法进行精确的末端执行器位姿估计。我们在仿真中表明，仅使用精确标注的人类演示，策略平均能达到使用机器人演示88.1%的性能；尽管存在固有的具身差距，当与机器人演示结合时，还能提升策略性能。
英文摘要：
Within the imitation learning paradigm, training generalist robots requires large-scale datasets obtainable only through diverse curation. Due to the relative ease to collect, human demonstrations constitute a valuable addition when incorporated appropriately. However, existing methods utilizing human demonstrations face challenges in inferring precise actions, ameliorating embodiment gaps, and fusing with frontier generalist robot training pipelines. In this work, building on prior studies that demonstrate the viability of using hand-held grippers for efficient data collection, we leverage the user's control over the gripper's appearance--specifically by assigning it a unique, easily segmentable color--to enable simple and reliable application of the RANSAC and ICP registration method for precise end-effector pose estimation. We show in simulation that precisely labeled human demonstrations on their own allow policies to reach on average 88.1% of the performance of using robot demonstrations, and boost policy performance when combined with robot demonstrations, despite the inherent embodiment gap.
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。
