Serifi, AgonGrandia, RubenKnoop, EspenGross, MarkusBächer, MoritzSkouras, MelinaWang, He2024-08-202024-08-2020241467-8659https://doi.org/10.1111/cgf.15175https://diglib.eg.org/handle/10.1111/cgf15175Recent progress in physics-based character control has made it possible to learn policies from unstructured motion data. However, it remains challenging to train a single control policy that works with diverse and unseen motions, and can be deployed to real-world physical robots. In this paper, we propose a two-stage technique that enables the control of a character with a full-body kinematic motion reference, with a focus on imitation accuracy. In a first stage, we extract a latent space encoding by training a variational autoencoder, taking short windows of motion from unstructured data as input. We then use the embedding from the time-varying latent code to train a conditional policy in a second stage, providing a mapping from kinematic input to dynamics-aware output. By keeping the two stages separate, we benefit from self-supervised methods to get better latent codes and explicit imitation rewards to avoid mode collapse. We demonstrate the efficiency and robustness of our method in simulation, with unseen user-specified motions, and on a bipedal robot, where we bring dynamic motions to the real world.CCS Concepts: Computing methodologies → Learning from demonstrations; Learning latent representations; Reinforcement learning; Physical simulation; Animation; Control methodsComputing methodologies → Learning from demonstrationsLearning latent representationsReinforcement learningPhysical simulationAnimationControl methods10.1111/cgf.1517511 pages