用视觉和力觉引导扩散策略，实现高精度电池撬动
Robotic Compliant Object Prying Using Diffusion Policy Guided by Vision and Force Observations
- 融合图像与低维力觉信号，增强扩散策略的感知能力
- 在多种场景下达96%成功率，比纯视觉方法提升57%
- 可零样本迁移至未见过的电池类型，适合复杂拆解任务
电动汽车及各类消费产品中电池应用日益广泛，亟需高效回收方案。这类产品常含柔性与刚性部件混合，机器人拆解是实现规模化回收的关键步骤。扩散策略在机器人低层技能学习中展现出潜力，但接触密集任务需引入力反馈以提升性能。本文将视觉与力觉信息结合，应用于柔性物体撬动任务。针对低维力信号与高维图像融合时信息被稀释的问题，提出一种有效整合机制。在要求高精度与多步执行的电池撬动任务上验证，模型在多样化场景中取得96%成功率，相较仅用视觉的基线提升57%。方法还具备零样本迁移能力，可处理未见过的物体与电池类型。项目视频与代码已公开。
原文摘要 · Abstract (English)
The growing adoption of batteries in the electric vehicle industry and various consumer products has created an urgent need for effective recycling solutions. These products often contain a mix of compliant and rigid components, making robotic disassembly a critical step toward achieving scalable recycling processes. Diffusion policy has emerged as a promising approach for learning low-level skills in robotics. To effectively apply diffusion policy to contact-rich tasks, incorporating force as feedback is essential. In this paper, we apply diffusion policy with vision and force in a compliant object prying task. However, when combining low-dimensional contact force with high-dimensional image, the force information may be diluted. To address this issue, we propose a method that effectively integrates force with image data for diffusion policy observations. We validate our approach on a battery prying task that demands high precision and multi-step execution. Our model achieves a 96\% success rate in diverse scenarios, marking a 57\% improvement over the vision-only baseline. Our method also demonstrates zero-shot transfer capability to handle unseen objects and battery types. Supplementary videos and implementation codes are available on our project website. https://rros-lab.github.io/diffusion-with-force.github.io/
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