LEAP: Enhancing Visuomotor Learning with Geometry Supervision
Overview
LEAP improves visuomotor policy learning by addressing geometry supervision limitations using an auxiliary decoder to reconstruct point clouds from visual features.
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- arXiv Robotics — research abstractsLEAP: Making Privileged Geometry Supervision Effective for Visuomotor Learning
The paper introduces LEAP, a framework that improves visuomotor policy learning by addressing limitations in privileged geometry supervision. It uses an auxiliary decoder to reconstruct point clouds from visual features while retaining proprioception for action prediction, leading to consistent improvements over existing methods like Diffusion Policy and ACT.
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