Summary of Cross-domain Policy Transfer by Representation Alignment Via Multi-domain Behavioral Cloning, By Hayato Watahiki et al.
Cross-Domain Policy Transfer by Representation Alignment via Multi-Domain Behavioral Cloning
by Hayato Watahiki, Ryo Iwase, Ryosuke Unno, Yoshimasa Tsuruoka
First submitted to arxiv on: 24 Jul 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 The proposed approach for cross-domain policy transfer learns a shared latent representation across domains and a common abstract policy on top of it, leveraging multi-domain behavioral cloning and maximum mean discrepancy (MMD) regularization. This method outperforms existing approaches in handling significant domain gaps or out-of-distribution tasks, achieving higher transfer performance. The technique involves training only one multi-domain policy, making extension easier than existing methods. Empirical evaluations demonstrate the efficacy of this approach across various domain shifts, including cross-morphology and cross-viewpoint settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots and computers learn new skills in different situations without needing to interact with an exact target setup. Current approaches struggle when there are big differences between domains or out-of-the-ordinary tasks. The researchers introduce a simple way to transfer learned skills across domains by learning a shared representation and common policy. They use behavioral cloning on proxy tasks and add a regularization term to keep the representations aligned. This approach works better than previous methods, especially in situations where exact domain translation is difficult. Tests show that this method can handle different domains well, including ones with varying shapes or viewpoints. |
Keywords
* Artificial intelligence * Regularization * Translation
