EgoGuide: Egocentric Guidance for Efficient Robot-Free Demonstration Collection and Learning
For robot learning from demonstrations, EgoGuide addresses data inefficiency and lack of global context in UMI-style pipelines, offering a more efficient and robust collection and learning framework.
EgoGuide introduces an egocentric data collection interface with online quality guidance and a Gated Egocentric Residual Policy, reducing required data episodes and improving robustness under occlusion in real-world robot learning.
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: https://silicx.github.io/EgoGuide