用扩散模型学习任意物体的变摩擦抓取，实现在真实机器人上高效精准操控。
Variable-Friction In-Hand Manipulation for Arbitrary Objects via Diffusion-Based Imitation Learning
- 基于扩散模型的模仿学习，结合仿真与少量真实数据联合训练。
- 2小时训练+1小时实测数据，对任意物体实现71.3%成功率，误差仅2.676mm和1.902度。
- 无需为每类物体定制策略，适合需要通用抓取能力的研究与应用。
针对任意物体的灵巧手内操纵（IHM）因复杂的接触过程而极具挑战。变摩擦操纵虽在二维场景中展现过鲁棒性与通用性，但传统硬编码方法仅适用于规则多边形，且目标姿态受限，需为每个物体单独设计策略。本文提出一种端到端学习方法，可在真实硬件上实现任意物体对任意目标姿态的精确操控，工程与数据收集成本极低。方法采用基于扩散模型的模仿学习，融合仿真与少量真实数据进行联合训练。实验表明，在单块A100 GPU上训练2小时、仅用1小时真实数据采集后，可成功操纵包括多边形与非多边形在内的任意物体，平均成功率71.3%，平均位姿误差达2.676 mm和1.902度，优于以往定制化物体特定策略。
原文摘要 · Abstract (English)
Dexterous in-hand manipulation (IHM) for arbitrary objects is challenging due to the rich and subtle contact process. Variable-friction manipulation is an alternative approach to dexterity, previously demonstrating robust and versatile 2D IHM capabilities with only two single-joint fingers. However, the hard-coded manipulation methods for variable friction hands are restricted to regular polygon objects and limited target poses, as well as requiring the policy to be tailored for each object. This paper proposes an end-to-end learning-based manipulation method to achieve arbitrary object manipulation for any target pose on real hardware, with minimal engineering efforts and data collection. The method features a diffusion policy-based imitation learning method with co-training from simulation and a small amount of real-world data. With the proposed framework, arbitrary objects including polygons and non-polygons can be precisely manipulated to reach arbitrary goal poses within 2 hours of training on an A100 GPU and only 1 hour of real-world data collection. The precision is higher than previous customized object-specific policies, achieving an average success rate of 71.3% with average pose error being 2.676 mm and 1.902 degrees.
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