Projects per year
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.
Original language | English |
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Pages (from-to) | 4700-4712 |
Journal | IEEE Transactions on Visualization and Computer Graphics |
Volume | 28 |
Issue number | 12 |
Early online date | 2021 |
DOIs | |
Publication status | Published - Dec 2022 |
MoE publication type | A1 Journal article-refereed |
Keywords
- action space
- Aerospace electronics
- hierarchical reinforcement learning
- movement optimization
- Optimization
- policy optimization
- Reinforcement learning
- Splines (mathematics)
- Task analysis
- Training
- trajectory optimization
- Trajectory optimization
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Dive into the research topics of 'Learning Task-Agnostic Action Spaces for Movement Optimization'. Together they form a unique fingerprint.Projects
- 1 Finished
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IMAI: Interactive Movement Artificial Intelligence (IMAI)
Hämäläinen, P., Babadi, A., Rajamäki, J., Kaos, M., Takatalo, J., Toikka, J., Ikkala, A. & Naderi, K.
01/09/2016 → 31/08/2020
Project: Academy of Finland: Other research funding