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Perpetual Humanoid Control for Real-time Simulated Avatars
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We present a physics-based humanoid controller that achieves high-fidelity motion imitation and fault-tolerant behavior in the presence of noisy input (e.g. pose estimates from video or generated from language) and unexpected falls. Our controller scales up to learning ten thousand motion clips without using any external stabilizing forces and learns to naturally recover from fail-state. Given reference motion, our controller can perpetually control simulated avatars without requiring resets. At its core, we propose the progressive multiplicative control policy (PMCP), which dynamically allocates new network capacity to learn harder and harder motion sequences. PMCP allows efficient scaling for learning from large-scale motion databases and adding new tasks, such as fail-state recovery, without catastrophic forgetting. We demonstrate the effectiveness of our controller by using it to imitate noisy poses from video-based pose estimators and language-based motion generators in a live and real-time multi-person avatar use case.
Forward citations
Cited by 4 Pith papers
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LooperMuscle: Fast and Stable Learning of Humanoid Whole-Body Tracking via Structured Mixture-of-Experts
LooperMuscle trains a humanoid whole-body tracking policy in about 45 minutes, cutting body error by 34% versus FastSAC and reaching 72% of PPO's reward, versus PPO's 6 hours.
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Generating Physically Realistic and Directable Human Motions from Multi-Modal Inputs
A single reinforcement-learned controller uses masked motion demonstrations to catch up, combine, and complete humanoid motions from sparse multi-modal directives.
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EMP: Executable Motion Prior for Humanoid Robot Standing Upper-body Motion Imitation
A state-conditioned executable motion prior network modifies upper-body motion targets so a humanoid can imitate human gestures while maintaining balance.
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