让机器人在复杂接触任务中实现空间泛化，靠的是对称性设计的视觉力觉策略。
EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Policy for Spatially Generalizable Contact-rich Tasks
- 构建分层策略：高层视觉规划+低层力控执行，全程保持空间对称性。
- 实测钉孔插入等任务成功率接近100%，可泛化到未见过的空间布局。
- 适合做高精度接触操作的机器人系统，尤其看重鲁棒性与泛化能力。
本文提出一种用于接触密集型操作任务的视觉驱动机器人策略框架，实现跨任务配置的空间泛化。聚焦于仅用少量示范训练的钉孔插入（PiH）任务，提出EquiContact框架，由高层视觉规划器（Diffusion Equivariant Descriptor Field, Diff-EDF）和新型低层柔顺视觉运动策略（Geometric Compliant ACT, G-CompACT）组成。G-CompACT仅使用局部观测（几何一致误差向量、力矩读数、腕装RGB图像），在末端执行器坐标系下输出动作。通过这些设计，整个EquiContact流程实现从感知到力控的SE(3)等变性。还提出三个关键要素：柔顺性、局部化策略、诱导等变性。真实世界实验在钉孔插入、拧螺丝、表面擦拭任务中均达到近乎完美的成功率，并能稳健泛化至未见空间构型，验证了该框架与原则的有效性。更多视频与细节请访问项目网站：https://equicontact.github.io/EquiContact-website/
原文摘要 · Abstract (English)
This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks that generalize spatially across task configurations. We focus on achieving robust spatial generalization of the policy for the peg-in-hole (PiH) task trained from a small number of demonstrations. We propose EquiContact, a hierarchical policy composed of a high-level vision planner (Diffusion Equivariant Descriptor Field, Diff-EDF) and a novel low-level compliant visuomotor policy (Geometric Compliant ACT, G-CompACT). G-CompACT operates using only localized observations (geometrically consistent error vectors (GCEV), force-torque readings, and wrist-mounted RGB images) and produces actions defined in the end-effector frame. Through these design choices, we show that the entire EquiContact pipeline is SE(3)-equivariant, from perception to force control. We also outline three key components for spatially generalizable contact-rich policies: compliance, localized policies, and induced equivariance. Real-world experiments on PiH, screwing, and surface wiping tasks demonstrate a near-perfect success rate and robust generalization to unseen spatial configurations, validating the proposed framework and principles. The experimental videos and more details can be found on the project website: https://equicontact.github.io/EquiContact-website/
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