Summary of Robust Policy Learning Via Offline Skill Diffusion, by Woo Kyung Kim et al.
Robust Policy Learning via Offline Skill Diffusion
by Woo Kyung Kim, Minjong Yoo, Honguk Woo
First submitted to arxiv on: 1 Mar 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 This paper proposes a novel offline skill learning framework called DuSkill, which employs a guided Diffusion model to generate versatile skills that can be extended from limited skills in datasets. The authors devise a guided diffusion-based skill decoder in conjunction with hierarchical encoding to disentangle the skill embedding space into domain-invariant behaviors and factors inducing domain variations. This framework enhances the diversity of skills learned offline, enabling accelerated learning of high-level policies for different domains. Experimental results show that DuSkill outperforms other skill-based imitation learning and RL algorithms on several long-horizon tasks, demonstrating its benefits in few-shot imitation and online RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn faster by creating new skills from old ones. It’s like taking a shortcut to get good at something new. The authors created a special way to make these skills more useful by making them better at handling different situations. They tested it and found that it works really well for learning new things quickly. |
Keywords
* Artificial intelligence * Decoder * Diffusion * Diffusion model * Embedding space * Few shot
