ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
This work addresses motion control in simulation for robotics or animation, offering a novel method but is incremental as it builds on existing VAE and model-based approaches.
The authors tackled the problem of learning generative motion control policies for physics-based characters by introducing ControlVAE, a model-based framework using variational autoencoders, which enables realistic human behavior generation and efficient skill reuse for downstream tasks.
In this paper, we introduce ControlVAE, a novel model-based framework for learning generative motion control policies based on variational autoencoders (VAE). 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. In the training of ControlVAE, we employ a learnable world model to realize direct supervision of the latent space and the control policy. This world model effectively captures the unknown dynamics of the simulation system, enabling efficient model-based learning of high-level downstream tasks. We also learn a state-conditional prior distribution in the VAE-based generative control policy, which generates a skill embedding that outperforms the non-conditional priors in downstream tasks. We demonstrate the effectiveness of ControlVAE using a diverse set of tasks, which allows realistic and interactive control of the simulated characters.