Explaining Reward Functions to Humans for Better Human-Robot Collaboration

Explainable AI can improve human-robot teamwork by helping humans understand what the robot is trying to accomplish. The paper argues that when humans can see and understand a robot's reward function (its goals and priorities), they can work together more effectively and teach the robot more efficiently.

Understanding gaps in a robot's knowledge is key to better collaboration. The research emphasizes that humans need to identify where a robot's understanding of human values falls short, which allows them to provide targeted feedback rather than general corrections—speeding up team performance improvements.

Different ways of explaining robot goals work better in different situations. The paper proposes studying multiple "modalities" (different formats like visual, textual, or interactive explanations) across domains of varying complexity, suggesting that a one-size-fits-all explanation approach won't work for all human-robot collaboration scenarios.

This research addresses a critical challenge in value alignment. The work tackles the practical problem of ensuring robots actually understand and pursue human values—a foundational concern for responsible AI deployment where misaligned robot objectives could lead to unhelpful or harmful assistance.

Systematic evaluation of explanation techniques is needed. The paper introduces assessment methods to measure how well humans actually understand robot reward functions, establishing a framework for testing which explanation approaches genuinely improve human comprehension rather than just appearing to do so.