通过约束抓取物体在低维流形上，提升操作类模仿学习的轨迹精度。
GrOMP: Grasped Object Manifold Projection for Multimodal Imitation Learning of Manipulation
- 将非刚性抓取物体限制在低维流形中，减少误差累积。
- 在4个精密装配任务中，结合触觉反馈显著提升操作精度。
- 无需额外数据，可适配多种模态，适合工业自动化场景。
模仿学习（IL）在重复性操作任务（如工业装配）中潜力巨大，但常因轨迹精度不足而受限，主要源于误差的逐次累积。本文提出抓取物体流形投影（GrOMP），一种交互式方法，通过将非刚性抓取的物体约束于低维流形，缓解此类问题。该方法假设机械臂持握一个可在抓取中发生可观测位移的物体，并需与固定部件精准对接。所有GrOMP改进均基于训练基础IL策略的同一专家数据集学习获得，并由基于多臂赌博机的交互组件动态调整。我们为GrOMP在文献中著名的误差累积边界上的改进提供了理论依据。在四个精确装配任务中，使用触觉反馈验证了该框架的有效性，且方法具有模态无关性。数据与视频见：williamvdb.github.io/GrOMPsite。
原文摘要 · Abstract (English)
Imitation Learning (IL) holds great potential for learning repetitive manipulation tasks, such as those in industrial assembly. However, its effectiveness is often limited by insufficient trajectory precision due to compounding errors. In this paper, we introduce Grasped Object Manifold Projection (GrOMP), an interactive method that mitigates these errors by constraining a non-rigidly grasped object to a lower-dimensional manifold. GrOMP assumes a precise task in which a manipulator holds an object that may shift within the grasp in an observable manner and must be mated with a grounded part. Crucially, all GrOMP enhancements are learned from the same expert dataset used to train the base IL policy, and are adjusted with an n-arm bandit-based interactive component. We propose a theoretical basis for GrOMP's improvement upon the well-known compounding error bound in IL literature. We demonstrate the framework on four precise assembly tasks using tactile feedback, and note that the approach remains modality-agnostic. Data and videos are available at williamvdb.github.io/GrOMPsite.
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