Learning Task-Agnostic Action Spaces for Movement Optimization
This provides a building block for movement optimization methods in animation and robotics, though it is incremental as it builds on existing parameterization approaches.
The paper tackles the problem of exploring dynamics for animated characters by learning a task-agnostic action space that simplifies movement optimization, resulting in improved efficiency across multiple tasks and algorithms without needing reference data.
We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous papers, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-level control policy that drives the agent's state towards the targets. Our novel contribution is that with our exploration data, we are able to learn the low-level policy in a generic manner and without any reference movement data. Trained once for each agent or simulation environment, the policy improves the efficiency of optimizing both trajectories and high-level policies across multiple tasks and optimization algorithms. We also contribute novel visualizations that show how using target states as actions makes optimized trajectories more robust to disturbances; this manifests as wider optima that are easy to find. Due to its simplicity and generality, our proposed approach should provide a building block that can improve a large variety of movement optimization methods and applications.