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Neuro-Symbolic Task Replanning using Large Language Models
- 주제(키워드) Task Planning , Large Language Models , Replanning
- 주제(기타) 로봇공학/로보틱스
- 설명문(URI) https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003176318
- 관리정보기술 faculty
- 등재 KCI등재
- 발행기관 한국로봇학회
- 발행년도 2025
- URI http://www.dcollection.net/handler/ewha/000000245064
- 본문언어 한국어
초록/요약
We introduce a novel task replanning algorithm that combines a symbolic task planner with a multimodal Large Language Model (LLM). Our algorithm starts by describing the scene by extracting the semantic and spatial relationships of objects in the environment through a multimodal LLM and an open-vocabulary object detection model. Then, the LLM formulates a planning problem in symbolic form based on the scene description and the user’s goal description, which are then processed by the symbolic planner to create task plans. These plans are converted into low-level executable codes for the robot, with the LLM performing syntax and semantic checks to ensure validity and facilitate replanning if necessary. We demonstrate the application of our replanning pipeline using dual UR5e manipulators in various benchmark tasks, including pick-and-place operations, block-stacking, and block rearrangement.
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