Summary of Slim: Skill Learning with Multiple Critics, by David Emukpere et al.
SLIM: Skill Learning with Multiple Critics
by David Emukpere, Bingbing Wu, Julien Perez, Jean-Michel Renders
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 paper proposes SLIM, a multi-critic learning approach for self-supervised skill learning in robotic manipulation. It addresses the limitations of existing methods by utilizing multiple critics to combine multiple reward functions, which improves latent-variable skill discovery and overcomes interference among rewards. The approach demonstrates applicability in tabletop manipulation, acquiring safe and efficient motor primitives through hierarchical reinforcement learning and planning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way for robots to learn how to do tasks without being told what to do. It’s called self-supervised learning, and it’s like when you learn a new skill by experimenting and trying things out. The method uses multiple “judges” to decide if the robot is doing something useful or not. This helps the robot learn faster and better than before. They tested this method with robots that can move objects on a table, and it worked really well! |
Keywords
* Artificial intelligence * Reinforcement learning * Self supervised
