TacDiffusion: 力領域拡散ポリシーによる高精度触覚マニピュレーション
TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation
拡散モデルで6軸力覚を生成し、単一タスクの実演から未学習の高精度挿入タスクへゼロショット転移する触覚組立フレームワークを提案。
著者: Yansong Wu, Zongxie Chen, Fan Wu, Lingyun Chen, Liding Zhang, Zhenshan Bing, Abdalla Swikir, Sami Haddadin, Alois Knoll
分類: cs.RO
原文アブストラクト
Assembly is a crucial skill for robots in both modern manufacturing and service robotics. However, mastering transferable insertion skills that can handle a variety of high-precision assembly tasks remains a significant challenge. This paper presents a novel framework that utilizes diffusion models to generate 6D wrench for high-precision tactile robotic insertion tasks. It learns from demonstrations performed on a single task and achieves a zero-shot transfer success rate of 95.7% across various novel high-precision tasks. Our method effectively inherits the self-adaptability demonstrated by our previous work. In this framework, we address the frequency misalignment between the diffusion policy and the real-time control loop with a dynamic system-based filter, significantly improving the task success rate by 9.15%. Furthermore, we provide a practical guideline regarding the trade-off between diffusion models' inference ability and speed.
