  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.
