MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations

no code implementations • 16 Oct 2023 • Heyuan Yao, Zhenhua Song, Yuyang Zhou, Tenglong Ao, Baoquan Chen, Libin Liu

In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations.

In-Context Learning Model-based Reinforcement Learning

ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters

no code implementations • 12 Oct 2022 • Heyuan Yao, Zhenhua Song, Baoquan Chen, Libin Liu

Our framework can learn a rich and flexible latent representation of skills and a skill-conditioned generative control policy from a diverse set of unorganized motion sequences, which enables the generation of realistic human behaviors by sampling in the latent space and allows high-level control policies to reuse the learned skills to accomplish a variety of downstream tasks.