Learning a Universal Humanoid Motion Representation for Versatile Physics-Based Control

Core Concepts

A universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control, learned by distilling from a large-scale motion imitator and using a learnable prior.

Abstract

The paper presents a method for learning a universal motion representation, called PULSE, that can be reused across a wide range of downstream tasks for physics-based humanoid control.
Key highlights:

The authors first train a motion imitator, called PHC+, that can imitate all motion sequences from a large-scale dataset (AMASS).
They then distill the motor skills of PHC+ into a latent space representation using a variational information bottleneck. This allows the latent space to inherit the comprehensive motor skills of the imitator.
To enhance the expressiveness of the latent space, the authors jointly learn a learnable prior conditioned on the humanoid's proprioception (pose and velocity). This prior enables the generation of diverse and stable human-like motion through random sampling.
The learned latent space is then used as the action space for downstream tasks, including generative tasks like speed control, striking, and complex terrain traversal, as well as motion tracking tasks like VR controller tracking.
Experiments show that the universal motion representation learned by PULSE outperforms state-of-the-art latent space models and training from scratch, demonstrating its effectiveness in solving a wide range of humanoid control tasks.

Stats

The AMASS dataset contains around 40 hours of motion capture data.
The authors train on the cleaned AMASS training set, which contains around 1 billion motion samples.
For testing, they use the AMASS test set as well as a real-world dataset from QuestSim with 14 sequences and 16 minutes of data.

Quotes

"We close this gap by significantly increasing the coverage of our motion representation space."
"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."

Key Insights Distilled From

Universal Humanoid Motion Representations for Physics-Based Control

by Zhengyi Luo,... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2310.04582.pdf

Deeper Inquiries

How can the learned motion representation be further extended to handle more complex interactions with the environment, such as object manipulation and multi-agent coordination

The learned motion representation can be extended to handle more complex interactions with the environment by incorporating additional layers of abstraction and context. For object manipulation tasks, the latent space model can be augmented with specific motor skills related to grasping, lifting, and manipulating objects. This can be achieved by training the model on a dataset that includes a wide range of object manipulation scenarios, allowing the latent space to capture the necessary motor skills for such interactions. Additionally, the model can be fine-tuned using reinforcement learning to adapt to different object shapes, sizes, and weights.
For multi-agent coordination tasks, the latent space can be expanded to include coordination and communication skills necessary for interacting with other agents. By training the model on scenarios that involve collaboration, competition, and coordination between multiple agents, the latent space can learn to generate coordinated motion patterns that facilitate effective interaction with other agents. Hierarchical reinforcement learning can be used to train policies that leverage the learned latent space for multi-agent coordination tasks, enabling the agents to exhibit complex and adaptive behaviors in dynamic environments.

What are the potential limitations of the variational information bottleneck approach, and how could it be improved to better preserve the full range of motor skills from the imitator

The variational information bottleneck approach, while effective in modeling the distribution of motor skills and ensuring a compact latent space, may have limitations in fully preserving the full range of motor skills from the imitator. One potential limitation is the trade-off between compression and reconstruction accuracy, where the bottleneck may discard some fine-grained details in order to achieve a more compact representation. To address this limitation, the approach could be improved by incorporating a more flexible and adaptive bottleneck structure that allows for variable compression levels based on the complexity of the motor skill being encoded.
Another limitation could be the sensitivity of the variational information bottleneck to the choice of hyperparameters, such as the balance between the reconstruction error and the KL divergence term. Fine-tuning these hyperparameters can be challenging and may require extensive experimentation. One way to improve this is to explore automated hyperparameter optimization techniques or adaptive learning rate schedules that dynamically adjust the trade-off during training based on the model's performance.

Could the learned motion prior be used to initialize or guide the exploration of other reinforcement learning tasks beyond humanoid control, such as in robotics or computer animation

The learned motion prior could be utilized to initialize or guide the exploration of other reinforcement learning tasks beyond humanoid control by serving as a transferable representation of motor skills. For robotics tasks, the motion prior could be used to initialize the policy for robotic manipulation tasks, enabling the robot to leverage the learned motor skills for more efficient and effective interaction with the environment. By transferring the knowledge encoded in the motion prior, the robot can start with a strong foundation of motor skills and adapt them to the specific task requirements through fine-tuning.
In computer animation, the motion prior could be applied to guide the exploration of character animation tasks, such as choreography or expressive motion generation. By using the learned motor skills as a starting point, animators and designers can quickly prototype and iterate on complex motion sequences with human-like behavior. The motion prior can provide a structured and coherent basis for generating diverse and realistic animations, reducing the time and effort required for manual keyframing and tweaking.