Summary of Language Guided Skill Discovery, by Seungeun Rho et al.
Language Guided Skill Discovery
by Seungeun Rho, Laura Smith, Tianyu Li, Sergey Levine, Xue Bin Peng, Sehoon Ha
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); 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 Skill discovery methods enable agents to learn diverse emergent behaviors without explicit rewards. To make learned skills useful for unknown downstream tasks, obtaining a semantically diverse repertoire of skills is essential. This paper introduces Language Guided Skill Discovery (LGSD), a skill discovery framework that leverages large language models (LLMs) to directly maximize the semantic diversity between skills. LGSD takes user prompts as input and outputs a set of semantically distinctive skills. The prompts constrain the search space into a semantically desired subspace, guiding the agent to visit semantically diverse states. We demonstrate LGSD’s effectiveness in enabling legged robots to visit different areas on a plane by changing prompts, and show that language guidance aids in discovering more diverse skills compared to existing skill discovery methods in robot-arm manipulation environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching machines to learn new things without being told what to do. The idea is to give the machine many different ways of doing things, so it can choose the best one for a task it hasn’t seen before. The researchers created a way to make this happen by using language models, which are like super-smart dictionaries. They showed that this method works well in teaching robots to move around and do different tasks. This is important because it means machines could learn to do many new things without needing to be specifically programmed for each one. |
