Article

ARC: Using Human Corrections to Guide Residual Correction of Robot Policies

The paper proposes Adaptive Residual Correction (ARC), which learns residual corrections via reinforcement learning on top of a frozen imitation policy, using sparse human corrections to decide when to activate residual control and in which direction of the residual action space to explore. On three precision manipulation tasks with a real robot, ARC raises average task success from 33.3% to 75.0%; the human corrections define 2-3 dimensional task-relevant subspaces of the seven-joint residual space, retaining 96.0-97.7% of the squared correction magnitude.

Part of the event: ARC: Human Corrections Guide Residual Correction of Robot Policies

This page provides a summary and source link only.