GR-BC mines better trajectories from imperfect demos, hitting ~91% success on precise manipulation
机器之心 · wechat · 2026-11-02
Zhejiang University and TeleAI researchers proposed Graph-Reweighted Behavior Cloning (GR-BC), accepted at CoRL 2026. It builds a graph over multiple teleoperation attempts with time and similarity edges, runs backward Dijkstra from success states to derive graph-retrieved progress, and weights action segments that show net progress without excessive regression—plugged directly into VLA supervision loss. On four real-robot tasks (part assembly, GPU insertion, screw tightening, RAM insertion) with X-VLA backbone it reaches 91% average success, 27 points above DAgger-like baselines, and GRP distinguishes success/failure with 0.97 endpoint gap.
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