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arXiv · 2009.10337

Learning Task-Agnostic Action Spaces for Movement Optimization

Amin Babadi·Michiel van de Panne·C. Karen Liu·Perttu Hämäläinen
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Abstract

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.

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BibTeXRIS

Amin Babadi, Michiel van de Panne, C. Karen Liu, Perttu Hämäläinen. 2020-09-22. Learning Task-Agnostic Action Spaces for Movement Optimization. https://arxiv.org/abs/2009.10337

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