SkillMimic: Learning Basketball Interaction Skills from Demonstrations
Yinhuai Wang, Qihan Zhao, Runyi Yu, Hok Wai Tsui, Ailing Zeng, Jing, Lin, Zhengyi Luo, Jiwen Yu, Xiu Li, Qifeng Chen, Jian Zhang, Lei Zhang, Ping, Tan

TL;DR
SkillMimic introduces a data-driven framework for learning diverse basketball interaction skills from demonstrations without manually designed rewards, enabling scalable, generalizable, and composable skills for complex tasks.
Contribution
It proposes a unified HOI imitation reward that captures interaction patterns, allowing a single policy to learn multiple skills and transition between them without skill-specific rewards.
Findings
Successfully masters diverse basketball skills including dribbling, layup, and shooting.
Enables skill composition for complex tasks like consecutive scoring.
Improves diversity and generalization as dataset size increases.
Abstract
Traditional reinforcement learning methods for human-object interaction (HOI) rely on labor-intensive, manually designed skill rewards that do not generalize well across different interactions. We introduce SkillMimic, a unified data-driven framework that fundamentally changes how agents learn interaction skills by eliminating the need for skill-specific rewards. Our key insight is that a unified HOI imitation reward can effectively capture the essence of diverse interaction patterns from HOI datasets. This enables SkillMimic to learn a single policy that not only masters multiple interaction skills but also facilitates skill transitions, with both diversity and generalization improving as the HOI dataset grows. For evaluation, we collect and introduce two basketball datasets containing approximately 35 minutes of diverse basketball skills. Extensive experiments show that SkillMimic…
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Taxonomy
TopicsPhysical Education and Pedagogy · Educational Games and Gamification · Online Learning and Analytics
