.png){kind=icon}
返回
SuperTrack: Motion Tracking for Physically Simulated Characters using Supervised Learning
.png){kind=icon}
PRE
AI
DOI：10.1145/3478513.3480527.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Motion Tracking
Motion Imitation
Imitation Learning
Reinforcement Learning
Character Animation
Motion Capture
期刊
IF:
9.5
论文数:
4.7K
被引数:
3.6W
机构
引用论文
.png){kind=icon}
Learning Basketball Dribbling Skills Using Trajectory Optimization and Deep Reinforcement Learning
PREAI
分享收藏
查看更多内容