Tracking large human-motion datasets has become the dominant route to general, expressive humanoid whole-body control, but scaling across many motions is expensive. Current pipelines use model-free reinforcement learning, either merging per-motion specialists through lossy distillation, or brute-forcing a single policy...
Qia-Yuan Liao, Yi-Yang Shao, Jun-Feng Long et al.· 1 citation
Precise end-effector tracking during humanoid whole-body motion is challenging due to floating-base oscillations, gravity, dynamic coupling, and locomotion-induced disturbances. We propose ResGAC, a whole-body humanoid controller for precise end-effector pose tracking that combines geometric admittance control (GAC) wi...
Joohwan Seo, Xiao-Feng Guo, Jin-Kun Cao et al.· 0 citations
This work introduces CoHuB (Collaborative Multi-Humanoid Benchmark), a simulation benchmark for multi-humanoid collaboration under egocentric visual observations and provides a foundation for developing and evaluating multi-humanoid collaboration policies.
Hyunji Park, Jebeom Chae, Minwoo Park et al.· 0 citations
Safe-Stop is proposed, a task-agnostic framework that pairs a learned stop policy with learned stoppability estimators, and yields decisions that are robust without sacrificing reactivity.
Jun-Feng Long, Pieter Abbeel, K. Sreenath et al.· 0 citations
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