大模型
arxiv.org ·
从语义决策到可行轨迹：面向窄空间泊车的自演进 LLM 引导最优控制
论文提出 SE-LLM-OCP 框架，将窄空间自动泊车任务分解为 LLM 高层离散机动决策与底层最优控制求解：LLM 在线生成稀疏机动计划，把泊车拆成一系列短时域轨迹优化问题，由底层求解器依次求解；求解失败时，LLM 汇总求解器与验证阶段的失败证据进行重规划。离线阶段，框架依据累积的在线失败自动从零演进结构化决策知识库。作者在类车模型和差速驱动机器人上进行了仿真验证，称该框架在狭窄场景下可实现更安全的自动泊车，并展示了同一机动表示向不同运动学平台的迁移。
AI推荐理由 arXiv 预印本，作者与摘要信息完整，方法描述具体（LLM 负责高层离散机动决策、最优控制模块保证动力学与碰撞约束、失败证据驱动重规划、离线自演进决策知识库），属于 LLM 与最优控制结合的机器人研究，对 Agent 规划与具身智能有参考价值。局限：仅仿真验证（类车模型与差速驱动机器人），无实车实验，效果为作者自述，未经同行评审，且摘要未给出量化指标。
72
正文
Computer Science > Robotics
arXiv:2609.24631 (cs)
[Submitted on 21 Sep 2026]
Title:From Semantic Decisions to Feasible Trajectories: Self-Evolving LLM-Guided Optimal Control for Narrow-Space Parking
Authors:Zhengbao Yao, Yuanfu Luo, Kehan Xue
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Abstract:Autonomous parking in nonconvex and narrow environments remains challenging. Although optimal-control methods can explicitly enforce vehicle dynamics and collision constraints, nonconvexity compromises solver robustness and can cause failures. Large language models (LLMs) exhibit strong semantic reasoning capabilities, but directly generating dense trajectories makes it difficult to guarantee physical feasibility. We introduce SE-LLM-OCP, a unified framework in which LLMs make high-level discrete maneuver decisions, while an optimal-control module enforces low-level vehicle dynamics and collision constraints. Online, the LLM proposes sparse maneuver plans, decomposing the parking task into a sequence of short-horizon trajectory-optimization problems. A low-level solver then sequentially solves optimal-control problems. If the solver fails, the LLM aggregates failure evidence from the solver and validation stages to guide replanning. Offline, SE-LLM-OCP automatically evolves a structured decision-making knowledge base from scratch, driven by accumulated online failures. We validate our proposed framework in simulation on a car-like vehicle model and on a differential-drive robot. Our experimental results show that SE-LLM-OCP enables safer autonomous parking in narrow scenarios and demonstrates transfer of the same maneuver representation to a different kinematic platform.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as:
arXiv:2609.24631 [cs.RO]
(or
arXiv:2609.24631v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.24631
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuanfu Luo [view email]
[v1]
Mon, 21 Sep 2026 14:12:27 UTC (5,827 KB)
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