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This paper explores how an embodied AI agent can act as a coach that accelerates human motor-skill development using reinforcement learning. The authors argue that effective coaching requires dynamically balancing guidance with learner autonomy — too much assistance leads to Over Reliance and skill atrophy, while too little leaves learners struggling.

Key findings:

  • An RL-based coaching policy that adapts its level of intervention to the learner's current skill level significantly accelerates skill acquisition compared to static assistance levels.
  • The AI coach that gradually fades scaffolding (consistent with Scaffolding theory in Intelligent Tutoring) produced the best long-term retention and transfer performance.
  • Over-reliance emerged when the coach provided excessive intervention, confirming the Over Reliance concern documented in Generative AI tutoring contexts.
  • Implications:

  • RL-based coaching offers a principled framework for personalized skill development in domains beyond traditional academics (e.g., surgical training, Professional Training, STEM lab skills).
  • The competence-based fading policy mirrors established pedagogical best practices, suggesting Formative Assessment signals can drive AI coaching adaptation.
  • Extends AI safety in tutoring research to embodied/motor skill domains.
  • Connected Concepts

  • Scaffolding
  • Adaptive Learning
  • Over Reliance
  • Intelligent Tutoring
  • Generative AI
  • Personalized Learning
  • Professional Training
  • STEM Education
  • Formative Assessment
  • Connected Articles

  • AI Tutor Safety Harms
  • Citation

    Wang, W., Gu, E., Loquercio, A., Hu, H., & Mangharam, R. (2026). AI Coaching for Accelerating Human Skill Development with Reinforcement Learning. arXiv:2606.25337. cs.RO.