Summary of One-shot Imitation in a Non-stationary Environment Via Multi-modal Skill, by Sangwoo Shin et al.
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill
by Sangwoo Shin, Daehee Lee, Minjong Yoo, Woo Kyung Kim, Honguk Woo
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 presents a novel skill-based imitation learning framework that enables one-shot imitation and zero-shot adaptation for complex tasks. The framework infers a semantic skill sequence from a single demonstration and optimizes each skill for environmental hidden dynamics. The approach leverages a vision-language model to learn a semantic skill set from offline video datasets, allowing for adaptation to different conditions and modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach someone how to do something new, like riding a bike. You show them how to balance, pedal, and steer once, and they have to figure it out from there. This is called one-shot imitation. It’s hard, especially when the environment changes or gets more complex. The researchers in this paper came up with a way to make one-shot imitation easier by breaking down complex tasks into smaller skills. They used a special kind of computer model that can understand both pictures and words to learn these skills from videos. This allowed them to adapt to changing environments and different demonstration conditions. |
Keywords
» Artificial intelligence » Language model » One shot » Zero shot
