Summary of Peac: Unsupervised Pre-training For Cross-embodiment Reinforcement Learning, by Chengyang Ying et al.
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
by Chengyang Ying, Zhongkai Hao, Xinning Zhou, Xuezhou Xu, Hang Su, Xingxing Zhang, Jun Zhu
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Designing generalizable agents that can adapt to diverse embodiments is a crucial challenge in Reinforcement Learning (RL), as it enables the deployment of RL agents in various real-world applications. The paper introduces Cross-Embodiment Unsupervised RL (CEURL), which leverages unsupervised learning to enable agents to acquire embodiment-aware and task-agnostic knowledge through online interactions within reward-free environments. CEURL is formulated as a novel Controlled Embodiment Markov Decision Process (CE-MDP) and analyzed under CE-MDP, leading to the development of a novel algorithm Pre-trained Embodiment-Aware Control (PEAC). PEAC provides an intuitive optimization strategy for cross-embodiment pre-training and can integrate flexibly with existing unsupervised RL methods. Extensive experiments in simulated (e.g., DMC and Robosuite) and real-world environments (e.g., legged locomotion) demonstrate that PEAC significantly improves adaptation performance and cross-embodiment generalization, effectively overcoming the unique challenges of CEURL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating robots that can adapt to different bodies. This is important because we want to use these robots in many real-world situations. The problem is that current methods only work well for specific tasks and not for adapting to new bodies. To solve this, the researchers developed a new method called Cross-Embodiment Unsupervised RL (CEURL). CEURL helps the robot learn how to adapt to different bodies without needing to do the same task over and over again. The team also created an algorithm that makes it easier for the robot to learn. They tested their approach on simulated robots and real-world robots, like those used in legged locomotion, and found that it worked much better than other methods. |
Keywords
» Artificial intelligence » Generalization » Optimization » Reinforcement learning » Unsupervised
