SCOPE: Scene-graph Construction Optimized by Plan Error-correction for Robot Long-term Planning Datasets

原文：SCOPE: Scene-graph Construction Optimized by Plan Error-correction for Robot Long-term Planning Datasets

作者：Chaoyue Li, Zeyu Gao, Yanfeng Lu, Fan Luo

来源：arXiv cs.RO（机器人）

正文

Computer Science > Robotics

arXiv:2610.31518v1 (cs)

[Submitted on 22 Oct 2026]

Title:SCOPE: Scene-graph Construction Optimized by Plan Error-correction for Robot Long-term Planning Datasets

Authors:Chaoyue Li, Zeyu Gao, Yanfeng Lu, Fan Luo

View a PDF of the paper titled SCOPE: Scene-graph Construction Optimized by Plan Error-correction for Robot Long-term Planning Datasets, by Chaoyue Li and 3 other authors

View PDF

HTML (experimental)

Abstract:Task and Motion Planning (TAMP) stands as a fundamental challenge in robotics, essential for enabling autonomous agents to execute complex, long-horizon tasks in diverse environments. While Large Language Models (LLMs) introduce a new paradigm for TAMP by leveraging extensive common sense, their inherent reasoning limitations hinder direct application, and fine-tuning strategies to enhance these capabilities are currently bottlenecked by the scarcity of high-quality datasets. Existing automated generation methods often suffer from homogeneity, limiting model generalization, and unsolvability, where hallucinated scene graphs create physical mismatches with task goals. To address these issues, we propose Scene-graph Construction Optimized by Plan Error-correction (SCOPE), an automated framework designed to generate diverse and executable TAMP datasets. SCOPE incorporates multi-level diversity metrics to maximize the information entropy of scenarios. Crucially, it introduces a Plan Feedback Error-correction pipeline that utilizes planner execution failures as feedback signals to retroactively rectify logical inconsistencies in scene graphs, ensuring physical feasibility. Experimental results demonstrate that SCOPE exhibits greater robustness to erroneous scene graphs and generates higher-quality data. When utilized for LLM fine-tuning, SCOPE-generated data yield a downstream TAMP model that achieves an absolute 12.33% gain in success rate over its counterpart fine-tuned with data generated by DELTA, a state-of-the-art PDDL-based planner.

Comments:

8 pages. Accepted at IROS 2026. Project page: this https URL

Subjects:

Robotics (cs.RO)

Cite as:

arXiv:2610.31518 [cs.RO]

(or

arXiv:2610.31518v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.31518

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

主题

机器人

由「前沿雷达」于 2026-10-23 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。