כתבה
arXiv cs.LG ·
AdaptManip: למידת תגובה תקיפה להרמת והעברת עצמים על ידי רובוטים זרוע-גוף
AdaptManip: Learning Adaptive Whole-Body Object Lifting and Delivery with Online Recurrent State Estimation
מסגרת Adaptive Whole-body Loco-Manipulation לרובוטים זרוע-גוף, המאפשרת הרמת והעברת עצמים באופן תקיפה.
תקציר מקורי באנגליתarXiv:2602.14363v2 Announce Type: replace-cross Abstract: This paper presents Adaptive Whole-body Loco-Manipulation, AdaptManip, a fully autonomous framework for humanoid robots to perform integrated navigation, object lifting, and delivery. Unlike prior imitation learning-based approaches that rely on human demonstrations and are often brittle to disturbances, AdaptManip aims to train a robust loco-manipulation policy via reinforcement learning without human demonstrations or teleoperation data. The proposed framework consists of three coupled components: (1) a recurrent object state estimator that tracks the manipulated object in real time under limited field-of-view and occlusions; (2) a whole-body base policy for robust locomotion with residual manipulation control for stable object li
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arxiv.org
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