Objective: Designing models and algorithms for manipulating human behavior during Human-Robot collaboration using natural language to improve trust, transparency, and team performance.
Phase 1 - RARE: We developed a novel mechanism, The Reward Augmentation and Repair through Explanation (RARE), for explanation-based reward coaching to improve human performance via Reinforcement Learning. RARE enables an autonomous system to detect model disparity between itself and a human collaborator, infer the source of the disagreement within the model, evaluate potential consequences of this error, and finally, provide human-interpretable feedback to encourage model correction. This process effectively enables a robot to provide a human with a policy update based on perceived model disparity, reducing the likelihood of costly or dangerous failures during joint task execution. We also proved using live human subject study that RARE makes robots more useful, helpful, and intelligent coaches.