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Universal Humanoid Motion Representations for Physics-Based Control
Zhengyi Luo, Jinkun Cao, Josh Merel, Alexander Winkler, Jing Huang, Kris Kitani, Weipeng Xu
PULSE learns a universal motion imitator from large unstructured mocap then builds a comprehensive physics-based motion representation.
Abstract
We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high dimensionality of humanoids and the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow range of movement styles (e.g. locomotion, game characters) from specialized motion datasets. This limited scope hampers their applicability in complex tasks. We close this gap by significantly increasing the coverage of our motion representation space. To achieve this, we first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator. This is achieved by using an encoder-decoder structure with a variational information bottleneck. Additionally, we jointly learn a prior conditioned on proprioception (humanoid's own pose and velocities) to improve model expressiveness and sampling efficiency for downstream tasks. By sampling from the prior, we can generate long, stable, and diverse human motions. Using this latent space for hierarchical RL, we show that our policies solve tasks using human-like behavior. We demonstrate the effectiveness of our motion representation by solving generative tasks (e.g.
How to read this
- Category
- Method / representation: universal motion latent space for physics-based control
- Contributions
- Learns a motion imitator that can reproduce all of human motion from a large, unstructured mocap dataset
- Distills skills from the imitator into a universal motion representation via an encoder-decoder with a variational information bottleneck
- Jointly learns a proprioception-conditioned prior enabling long, stable, diverse motion generation and hierarchical RL on downstream tasks
- Context
- Builds directly on Luo et al.'s Perpetual Humanoid Control, broadening prior narrow, style-specific skill embeddings into a comprehensive universal representation (PULSE).Builds on: Perpetual Humanoid Control for Real-time Simulated Avatars
- Correctness
- Assumes a strong full-coverage imitator can be distilled into a reusable latent without losing skill breadth; demonstrated for downstream hierarchical RL control, though the abstract is truncated, so check the paper for which tasks, coverage limits, and failure modes apply.
- Clarity
- Conceptually dense (VAE bottleneck, distillation, hierarchical RL); a first pass conveys the imitate-then-distill idea, later passes are needed for the latent-space and prior details.
- How to read it
- Focus on the two-stage imitate-then-distill design and the role of the variational bottleneck and proprioceptive prior; a second pass on the representation pays off if reusing it as a downstream control prior.
Built upon by
Nothing yet.
Related work
- Perpetual Humanoid Control for Real-time Simulated Avatars 2023 / ICCV
- CLoSD: Closing the Loop between Simulation and Diffusion for Multi-Task Character Control 2024 / arXiv
- CALM: Conditional Adversarial Latent Models for Directable Virtual Characters 2023 / SIGGRAPH
- SAME: Skeleton-Agnostic Motion Embedding for Character Animation 2023 / SIGGRAPH Asia
Keywords
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