Summary of Conceptagent: Llm-driven Precondition Grounding and Tree Search For Robust Task Planning and Execution, by Corban Rivera et al.
ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution
by Corban Rivera, Grayson Byrd, William Paul, Tyler Feldman, Meghan Booker, Emma Holmes, David Handelman, Bethany Kemp, Andrew Badger, Aurora Schmidt, Krishna Murthy Jatavallabhula, Celso M de Melo, Lalithkumar Seenivasan, Mathias Unberath, Rama Chellappa
First submitted to arxiv on: 8 Oct 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 Recent advances in perception algorithms and Large Language Models (LLMs) have shown promise in robotic planning and execution in open-world environments. However, previous work has failed to address the issue of hallucinations from LLMs, which can result in logical fallacies and failures to execute planned actions. To overcome these limitations, we introduce ConceptAgent, a natural language-driven robotic platform designed for task execution in unstructured environments. ConceptAgent includes innovations such as Predicate Grounding to prevent and recover from infeasible actions, and an embodied version of LLM-guided Monte Carlo Tree Search with self-reflection. Our simulation experiments demonstrate the effectiveness of ConceptAgent, achieving a 19% task completion rate across three room layouts and 30 easy level embodied tasks, outperforming state-of-the-art LLM-driven reasoning baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine robots that can plan and execute tasks in real-world environments. This is a challenging problem because there are many possible actions the robot could take, and it’s hard to predict what will happen next. Recent advances in artificial intelligence have helped with this problem, but there’s still room for improvement. We’ve developed a new system called ConceptAgent that can help robots make better decisions. It uses natural language processing and machine learning algorithms to plan and execute tasks. In tests, our system was able to complete 19% of tasks successfully, which is much better than previous systems. |
Keywords
» Artificial intelligence » Grounding » Machine learning » Natural language processing
