AI教练通过动态调整辅助程度，加速人类技能学习。
AI Coaching for Accelerating Human Skill Development with Reinforcement Learning
- 设计自适应辅助机制，根据学习者水平动态调整支持
- 用户研究显示学习效率比现有方法提升显著（N=33）
- 适合希望高效掌握复杂技能的用户或教育场景
AI协作者可通过共控显著提升人类表现，但过度协助会导致依赖与技能退化。本文研究具身AI作为教练如何加速人类运动技能发展。我们认为有效教练需基于学习者能力进行策略性支架搭建与适时撤回，允许产生有益的失败以促进学习。将交互式AI教练过程形式化为非合作动态博弈，学习者优化任务表现，教练则聚焦于提升学习者的独立能力。基于此框架，我们提出结合自适应共控与教练因果影响的概率模型的强化学习方法，实现教练策略的可训练性。在第一人称视角无人机竞速任务上的综合用户研究（N=33）表明，该方法在人类学习成果上显著优于当前最优的AI教练基线。
原文摘要 · Abstract (English)
AI copilots can substantially boost human performance through shared control, but excessive assistance can induce over-reliance and skill atrophy. This paper studies how an embodied AI agent can act as a coach that accelerates human motor-skill development. We argue that effective coaching requires strategic scaffolding and stepping back that are aligned with the learner's capability, allowing productive failures that drive learning. We formalize the interactive AI coaching process as a non-cooperative dynamic game in which the learner optimizes task performance while the coach targets the learner's independent competence. Building on this formalism, we develop a reinforcement learning framework combining adaptive shared control with probabilistic models of the coach's causal influence on skill evolution, enabling tractable training of coaching policies. A comprehensive user study (N=33) on first-person-view drone racing shows significant gains in human learning outcomes over state-of-the-art AI coaching baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。