Summary of Helper-x: a Unified Instructable Embodied Agent to Tackle Four Interactive Vision-language Domains with Memory-augmented Language Models, by Gabriel Sarch et al.
HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models
by Gabriel Sarch, Sahil Somani, Raghav Kapoor, Michael J. Tarr, Katerina Fragkiadaki
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 an extension of the instructable agent HELPER, which utilizes memory-augmented Large Language Models (LLMs) as task planners. The authors expand HELPER’s memory by incorporating a broader range of examples and prompts, allowing the agent to operate across multiple domains, including dialogue-based plan execution, natural language instruction following, active question asking, and commonsense room reorganization. The model, dubbed HELPER-X, achieves state-of-the-art performance on four diverse interactive visual-language embodied agent benchmarks: ALFRED, TEACh, DialFRED, and Tidy Task. Notably, HELPER-X performs well without requiring in-domain training, and remains competitive with agents that have undergone such training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HELPER is an instructable agent that uses memory-augmented Large Language Models (LLMs) as task planners. This means it can take instructions and use them to improve its language understanding and plan execution. The new version, HELPER-X, has a bigger memory with more examples and prompts, which lets it work in different areas like planning from dialogue or asking questions. The agent was tested on four different tasks and performed very well, even without special training for each task. |
Keywords
» Artificial intelligence » Language understanding
