ExploreVLM: Closed-Loop Robot Exploration Task Planning with Vision-Language Models

Zhichen Lou; Kechun Xu; Zhongxiang Zhou; Rong Xiong

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arXiv · 2508.11918

ExploreVLM: Closed-Loop Robot Exploration Task Planning with Vision-Language Models

Zhichen Lou·Kechun Xu·Zhongxiang Zhou·Rong Xiong

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Abstract

The advancement of embodied intelligence is accelerating the integration of robots into daily life as human assistants. This evolution requires robots to not only interpret high-level instructions and plan tasks but also perceive and adapt within dynamic environments. Vision-Language Models (VLMs) present a promising solution by combining visual understanding and language reasoning. However, existing VLM-based methods struggle with interactive exploration, accurate perception, and real-time plan adaptation. To address these challenges, we propose ExploreVLM, a novel closed-loop task planning framework powered by Vision-Language Models (VLMs). The framework is built around a step-wise feedback mechanism that enables real-time plan adjustment and supports interactive exploration. At its core is a dual-stage task planner with self-reflection, enhanced by an object-centric spatial relation graph that provides structured, language-grounded scene representations to guide perception and planning. An execution validator supports the closed loop by verifying each action and triggering re-planning. Extensive real-world experiments demonstrate that ExploreVLM significantly outperforms state-of-the-art baselines, particularly in exploration-centric tasks. Ablation studies further validate the critical role of the reflective planner and structured perception in achieving robust and efficient task execution.

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Zhichen Lou, Kechun Xu, Zhongxiang Zhou, Rong Xiong. 2025-08-16. ExploreVLM: Closed-Loop Robot Exploration Task Planning with Vision-Language Models. https://arxiv.org/abs/2508.11918

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