EgoGuide: Egocentric Guidance for Efficient Robot-Free Demonstration Collection and Learning

Abstract:Robot learning from real-world demonstrations is currently constrained by data scaling. Universal Manipulation Interface (UMI) provides an efficient robot-free data collection interface, yet current UMI-style pipelines often collect redundant demonstrations and lack global scene context. To improve data efficiency, we present EgoGuide, a collection interface that records synchronized wrist and head/egocentric observations and couples them with online visual-geometric data quality guidance. We also introduce a Gated Egocentric Residual Policy for robust learning from a viewpoint-varying egocentric camera, allowing head/egocentric context to correct ambiguous local observations while preserving stable wrist-view control. Real-world experiments show that EgoGuide reduces the required number of data episodes and improves data efficiency. The residual policy further improves robustness under visual occlusion. Project Page: this https URL

Robotics (cs.RO)

arXiv:2606.14665 [cs.RO]

(or arXiv:2606.14665v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.14665 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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