Junlan Xiao

The work proposes CARE, which collects failed rollouts, models stage-conditioned post-failure geometric deviation distributions, synthesizes representative failure states and corrective demonstrations from them, and at inference combines stage-wise planning with physically grounded 3D point-cloud monitoring to trigger atomic adjustments or re-operations; across RoboTwin 2.0, RoboFactory, the newly introduced FSR-Bench, and real-world dual-arm tasks it reports average task-success gains of 14.5 points in simulation, 15.9 points in the real world, and a 7.5-point gain in recovery success on FSR-Bench.

The work proposes CARE, which collects failed rollouts, models stage-conditioned post-failure geometric deviation distributions, synthesizes representative failure states and corrective demonstrations from them, and at inference combines stage-wise planning with physically grounded 3D point-cloud monitoring to trigger atomic adjustments or re-operations; across RoboTwin 2.0, RoboFactory, the newly introduced FSR-Bench, and real-world dual-arm tasks it reports average task-success gains of 14.5 points in simulation, 15.9 points in the real world, and a 7.5-point gain in recovery success on FSR-Bench.