CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion
| Autor: | Jonathan Hans Soeseno, Trista Pei-Chun Chen, Wei-Chao Chen, Ying-Sheng Luo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Autonomous agent Process (computing) Machine Learning (stat.ML) 020207 software engineering 02 engineering and technology Animation Computer Graphics and Computer-Aided Design Motion capture Graphics (cs.GR) Motion (physics) Machine Learning (cs.LG) Computer Science - Graphics Human–computer interaction Statistics - Machine Learning User control 0202 electrical engineering electronic engineering information engineering Character animation Reinforcement learning |
| Popis: | Motion synthesis in a dynamic environment has been a long-standing problem for character animation. Methods using motion capture data tend to scale poorly in complex environments because of their larger capturing and labeling requirement. Physics-based controllers are effective in this regard, albeit less controllable. In this paper, we present CARL, a quadruped agent that can be controlled with high-level directives and react naturally to dynamic environments. Starting with an agent that can imitate individual animation clips, we use Generative Adversarial Networks to adapt high-level controls, such as speed and heading, to action distributions that correspond to the original animations. Further fine-tuning through the deep reinforcement learning enables the agent to recover from unseen external perturbations while producing smooth transitions. It then becomes straightforward to create autonomous agents in dynamic environments by adding navigation modules over the entire process. We evaluate our approach by measuring the agent's ability to follow user control and provide a visual analysis of the generated motion to show its effectiveness. Project page available at https://inventec-ai-center.github.io/projects/CARL/index.html |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|