Summary of Zero-shot Offline Imitation Learning Via Optimal Transport, by Thomas Rupf et al.
Zero-Shot Offline Imitation Learning via Optimal Transport
by Thomas Rupf, Marco Bagatella, Nico Gürtler, Jonas Frey, Georg Martius
First submitted to arxiv on: 11 Oct 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 This paper introduces a novel method for zero-shot imitation learning that addresses the issue of myopic behavior in existing approaches. The authors propose lifting a goal-conditioned value function to a distance between occupancies, which are approximated via a learned world model. This approach enables non-myopic, zero-shot imitation and can learn from offline, suboptimal data. The method is evaluated on complex, continuous benchmarks, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn new behaviors just by watching one demonstration. Right now, it’s hard for machines to do this without making mistakes or getting stuck in a loop. The authors came up with a new way to make sure the machine doesn’t get stuck and can still learn from imperfect data. This is important because it could help machines learn how to do things they’ve never done before just by watching someone else. |
Keywords
* Artificial intelligence * Zero shot
