PolyFit：一种通过仿真到现实自适应实现未见多边形形状轴孔装配的框架
PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation
AI总结：
PolyFit提出基于力/力矩监督学习的5自由度轴孔装配框架，通过多点接触策略和仿真到现实自适应，实现对未见多边形形状的高成功率装配。
摘要：
本研究针对机器人技术中基础且具有挑战性的轴孔装配任务，其中由传感器误差和机械误差引起的错位常常导致插入失败或卡阻。本研究提出了PolyFit，代表了从强化学习方法向监督学习方法转变的范式转换。PolyFit是一个基于力/力矩（F/T）的监督学习框架，专为5自由度轴孔装配设计。它利用F/T数据进行精确的外部位姿估计，并调整轴件位姿以纠正错位。在仿真环境中进行了大量训练，所使用的数据集涵盖了多种多样的轴孔形状、外部位姿及其对应的接触F/T读数。为了增强外部位姿估计，模型输入中集成了多点接触策略，因为相同的F/T读数可能对应不同的位姿。本研究提出了一种面向实际应用的仿真到现实自适应方法，利用仿真-真实配对数据集实现对复杂且未见多边形形状的有效泛化。PolyFit在仿真中对已见和未见形状分别取得了97.3%和96.3%的出色轴孔装配成功率。真实世界评估进一步展示了86.7%和85.0%的显著成功率，凸显了所提方法的鲁棒性和适应性。
英文摘要：
The study addresses the foundational and challenging task of peg-in-hole assembly in robotics, where misalignments caused by sensor inaccuracies and mechanical errors often result in insertion failures or jamming. This research introduces PolyFit, representing a paradigm shift by transitioning from a reinforcement learning approach to a supervised learning methodology. PolyFit is a Force/Torque (F/T)-based supervised learning framework designed for 5-DoF peg-in-hole assembly. It utilizes F/T data for accurate extrinsic pose estimation and adjusts the peg pose to rectify misalignments. Extensive training in a simulated environment involves a dataset encompassing a diverse range of peg-hole shapes, extrinsic poses, and their corresponding contact F/T readings. To enhance extrinsic pose estimation, a multi-point contact strategy is integrated into the model input, recognizing that identical F/T readings can indicate different poses. The study proposes a sim-to-real adaptation method for real-world application, using a sim-real paired dataset to enable effective generalization to complex and unseen polygon shapes. PolyFit achieves impressive peg-in-hole success rates of 97.3% and 96.3% for seen and unseen shapes in simulations, respectively. Real-world evaluations further demonstrate substantial success rates of 86.7% and 85.0%, highlighting the robustness and adaptability of the proposed method.
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。
