InFeR retrains imitation learning policies with a VIB loss for OOD failure detection and applies Grad-CAM to localize failure sources, enabling heuristic recovery in visual navigation without additional demonstrations.
Error-aware imitation learning from teleopera- tion data for mobile manipulation
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TER-DAgger uses force-prediction mismatches to trigger human corrections and residual-policy training, lifting precision-insertion success from 40.0% to 77.2% on average.
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InFeR: Informed Failure Resilience in Learned Visual Navigation Control
InFeR retrains imitation learning policies with a VIB loss for OOD failure detection and applies Grad-CAM to localize failure sources, enabling heuristic recovery in visual navigation without additional demonstrations.
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Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
TER-DAgger uses force-prediction mismatches to trigger human corrections and residual-policy training, lifting precision-insertion success from 40.0% to 77.2% on average.