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SuperTrack: Motion Tracking for Physically Simulated Characters using Supervised Learning
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AI
DOI：10.1145/3478513.3480527.png)
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Abstract
En 中文
In this paper we show how the task of motion tracking for physically simulated characters can be solved using supervised learning and optimizing a policy directly via back-propagation. To achieve this we make use of a world model trained to approximate a specific subset of the environment's transition function, effectively acting as a differentiable physics simulator through which the policy can be optimized to minimize the tracking error. Compared to popular model-free methods of physically simulated character control which primarily make use of Proximal Policy Optimization (PPO) we find direct optimization of the policy via our approach consistently achieves a higher quality of control in a shorter training time, with a reduced sensitivity to the rate of experience gathering, dataset size, and distribution.
Keywords:
Motion Tracking
Motion Imitation
Imitation Learning
Reinforcement Learning
Character Animation
Motion Capture
Journal
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4.7K
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3.6W