SkillMimic proposes multiplicative rewards and hierarchical skill control
What happened
SkillMimic proposes a multiplicative Gaussian kernel reward that treats pose, interaction, and contact as multiplicative joint conditions, learning 19 types of interaction skills from basketball demonstrations. Its hierarchical version uses discrete skill selection to drive a frozen low-level policy and reproduces four types of tasks on a new-generation simulation platform.
AI 根据报道生成 · 6 days ago更新
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- GuyuehomeSkillMimic uses multiplicative rewards and hierarchical skill control to imitate basketball skills
SkillMimic uses multiplicative Gaussian kernel rewards to turn pose, interaction, and contact into multiplicative joint conditions, learning 19 types of interactive skills from basketball demonstrations. Its hierarchical version uses discrete skill selection to drive a frozen low-level policy and reproduces four types of tasks on a new-generation simulation platform.
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