AI RESEARCH
Learning Actionable Manipulation Recovery via Counterfactual Failure Synthesis
arXiv CS.CV
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ArXi:2603.13528v1 Announce Type: cross While recent foundation models have significantly advanced robotic manipulation, these systems still struggle to autonomously recover from execution errors. Current failure-learning paradigms rely on either costly and unsafe real-world data collection or simulator-based perturbations, which