Learning from Corrective Demonstrations

Abstract

Robots deployed in human environments will inevitably encounter unmodeled scenarios which are likely to result in execution failures. To address this issue, we would like to allow co-present naive users to correct and improve the robot's behavior as these edge cases are encountered over time.