CLIC uses set-valued action targets from interactive human corrections instead of pointwise labels to train more robust imitation learning policies.
Energy-based contact planning under uncertainty for robot air hockey,
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From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback
CLIC uses set-valued action targets from interactive human corrections instead of pointwise labels to train more robust imitation learning policies.