用扩散模型生成6维力信号，实现高精度触觉抓取零样本迁移。
TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation
- 基于扩散模型生成6维力指令，从单任务演示中学习。
- 跨新任务零样本成功率95.7%，相比基线提升9.15%。
- 提出推理速度与性能权衡指南，适合高精度装配场景。
装配是现代制造和服务机器人中的关键技能。然而，掌握可迁移的高精度插入技能仍面临重大挑战。本文提出一种新框架，利用扩散模型生成6维力信号，用于高精度触觉机器人插入任务。该方法仅需单一任务的示范数据，即可在多种新型高精度任务上实现95.7%的零样本迁移成功率。我们的方法有效继承了此前工作所展示的自适应能力。针对扩散策略与实时控制环之间频率不匹配的问题，我们引入基于动态系统的滤波器，使任务成功率显著提升9.15%。此外，我们还提供了关于扩散模型推理能力与速度之间权衡的实际指导建议。
原文摘要 · Abstract (English)
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.
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