AgentCoach: LLM-Based Adaptive Coaching Feedback for Motor Skill Learning

We present AgentCoach, an LLM-powered system that provides adaptive feedback for motor skill learning from tutorial videos. The system works by extracting key coaching points (CPs) and compiling CP-specific evaluators that map each cue to measurable kinematic parameters. This process allows AgentCoach to connect high-level semantic meaning with low-level postural estimation for accurate, context-aware evaluation. During practice, learners receive concise visual diagnostics of their mistakes paired with prescriptive verbal feedback that adapts based on their performance history. We technically validate the CP extraction and evaluator compilation across a wide range of common sports and exercise videos. A user study confirms the system’s usability and shows the system’s potential effectiveness of its adaptive feedback across multiple skills.