aiqbench analysis
The paper applies learned, vision-based policies to contact-rich robot manipulation, with the core contribution being equivariance built into the learning pipeline so policies transfer to new positions and orientations without retraining. Its real-world peg-in-hole results are promising, but they come from a limited set of tasks and physical setups, so readers should treat the generalization claims as validated for those cases rather than broadly. For ML readers, the most useful takeaway is the design pattern of localized observations, geometric error signals, and induced equivariance.
EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Framework for Spatially Generalizable Contact-rich Tasks
arXiv:2507.10961v5 Announce Type: replace Abstract: This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks, which generalize spatially across task configurations. We propose EquiContact, a novel hierarchical framework composed of a high-level vision planner, such as a Diffusion Equivariant Descriptor Field (Diff-EDF) and a low-level compliant visuomotor policy, such as a Geometric Compliant Action Chunking Transformer (G-CompACT). G-CompACT, operating using localized observations from wrist-mounted RGB images, end-effector pose feedback in the form of a geometrically consistent error vector (GCEV) and force-torque readings, produces actions defined on the end-effector frame. We show that the entire EquiContact pipeline is $\SE$-equivariant under equivariant reference-frame estimation and left-invariant visual features, from perception to force control. Once the robot learns to execute a contact-rich task, such as inserting a peg-in-hole (PiH), it can execute similar tasks at different locations and orientations without retraining of the low-level policy. We also describe three key components for attaining spatially generalizable contact-rich policies: geometrically consistent compliance, the use of localized policies, and induced equivariance. Real-world peg-in-hole (PiH) experiments demonstrate near-perfect success rates under spatially out-of-distribution configurations, validating the proposed framework and principles, while additional screwing and surface-wiping experiments evaluate the transfer of the localized policy design beyond insertion. The experimental videos and more details can be found on the project website: https://equicontact.github.io/EquiContact-website/
Abstract:This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks, which generalize spatially across task configurations. We propose EquiContact, a novel hierarchical framework composed of a high-level vision planner, such as a Diffusion Equivariant Descriptor Field (Diff-EDF) and a low-level compliant visuomotor policy, such as a Geometric Compliant Action Chunking Transformer (G-CompACT). G-CompACT, operating using localized observations from wrist-mounted RGB images, end-effector pose feedback in the form of a geometrically consistent error vector (GCEV) and force-torque readings, produces actions defined on the end-effector frame. We show that the entire EquiContact pipeline is $\SE$-equivariant under equivariant reference-frame estimation and left-invariant visual features, from perception to force control. Once the robot learns to execute a contact-rich task, such as inserting a peg-in-hole (PiH), it can execute similar tasks at different locations and orientations without retraining of the low-level policy. We also describe three key components for attaining spatially generalizable contact-rich policies: geometrically consistent compliance, the use of localized policies, and induced equivariance. Real-world peg-in-hole (PiH) experiments demonstrate near-perfect success rates under spatially out-of-distribution configurations, validating the proposed framework and principles, while additional screwing and surface-wiping experiments evaluate the transfer of the localized policy design beyond insertion. The experimental videos and more details can be found on the project website: this https URL
Robotics (cs.RO)
arXiv:2507.10961 [cs.RO]
(or arXiv:2507.10961v5 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2507.10961 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 305 words