Summary of Semantic Skill Grounding For Embodied Instruction-following in Cross-domain Environments, by Sangwoo Shin et al.
Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments
by Sangwoo Shin, Seunghyun Kim, Youngsoo Jang, Moontae Lee, Honguk Woo
First submitted to arxiv on: 2 Aug 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 framework called Semantic Skill Grounding (SemGro) that addresses the challenge of grounding pretrained skills from embodied instruction-following (EIF) in different domains. The authors recognize that these skills are intricately entangled with domain-specific knowledge, making it difficult to apply them universally. SemGro leverages the hierarchical nature of semantic skills, decomposing them iteratively to ground each skill at an executable level within a target domain. The framework uses language models’ reasoning capabilities for composing and decomposing semantic skills, as well as their multi-modal extension for assessing feasibility in the target domain. The authors demonstrate the efficacy of SemGro in 300 cross-domain EIF scenarios using the VirtualHome benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a way to make artificial intelligence (AI) learn new skills that can be applied across different areas, like home automation or robotics. Right now, AI models are very good at following instructions, but they struggle to apply these skills in new and unfamiliar contexts. The authors created a system called Semantic Skill Grounding (SemGro) that helps AI models adapt their skills to new situations. SemGro works by breaking down complex skills into smaller, more manageable parts, and then figuring out how to use those skills in different domains. The researchers tested their system on 300 scenarios and found that it was very effective. |
Keywords
» Artificial intelligence » Grounding » Multi modal
