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

• URL: http://arxiv.org/abs/2210.06063v1
• Date: Wed, 12 Oct 2022 10:11:36 GMT
• Title: ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
• Authors: Heyuan Yao, Zhenhua Song, Baoquan Chen, Libin Liu
• Abstract summary: We introduce ControlVAE, a 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. We demonstrate the effectiveness of ControlVAE using a diverse set of tasks, which allows realistic and interactive control of the simulated characters.
• Score: 28.446959320429656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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