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Learning Task-Agnostic Action Spaces for Movement Optimization.

Amin Babadi, Michiel van de Panne, C Karen Liu

IEEE Transactions on Visualization and Computer Graphics

|July 27, 2021

Summary

This summary is machine-generated.

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We developed a new method to learn character movement policies without reference data. This approach simplifies movement optimization for various tasks and improves robustness to disturbances.

Area of Science:

Computer Graphics
Robotics
Machine Learning

Background:

Physically based animation requires efficient movement optimization.
Existing methods often rely on reference motion data or task-specific policies.

Purpose of the Study:

To propose a novel, task-agnostic method for learning character movement.
To simplify and enhance the efficiency of movement optimization for animated characters.

Main Methods:

Learning a low-level control policy using exploration data.
Parameterizing actions as target states for goal-conditioned learning.
Developing generic policies trained once per agent or environment.

Main Results:

The learned policy enables efficient optimization of trajectories and high-level policies across tasks.

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Novel visualizations demonstrate increased robustness of optimized trajectories to disturbances.

The approach achieves generic learning without requiring reference movement data.

Conclusions:

The proposed method offers a general and simple building block for movement optimization.
This approach can improve various movement optimization methods and applications.
Task-agnostic action spaces enhance the efficiency and robustness of character animation.