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arXiv · 2511.20330

ArtiBench and ArtiBrain: Benchmarking Generalizable Vision-Language Articulated Object Manipulation

Yuhan Wu·Tiantian Wei·Shuo Wang·ZhiChao Wang·Yanyong Zhang·Daniel Cremers·Yan Xia
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Abstract

Interactive articulated manipulation requires long-horizon, multi-step interactions with appliances while maintaining physical consistency. Existing vision-language and diffusion-based policies struggle to generalize across parts, instances, and categories. We first introduce ArtiBench, a five-level benchmark covering kitchen, storage, office, and tool environments. ArtiBench enables structured evaluation from cross-part and cross-instance variation to long-horizon multi-object tasks, revealing the core generalization challenges of articulated object manipulation. Building on this benchmark, we propose ArtiBrain, a modular framework that unifies high-level reasoning with adaptive low-level control. ArtiBrain uses a VLM-based Task Reasoner (GPT-4.1) to decompose and validate subgoals, and employs a Hybrid Controller that combines geometry-aware keyframe execution with affordance-guided diffusion for precise and interpretable manipulation. An Affordance Memory Bank continually accumulates successful execution episodes and propagates part-level actionable affordances to unseen articulated parts and configurations. Extensive experiments on ArtiBench show that our ArtiBrain significantly outperforms state-of-the-art multimodal and diffusion-based methods in robustness and generalization. Code and dataset will be released upon acceptance.

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Yuhan Wu, Tiantian Wei, Shuo Wang, ZhiChao Wang, Yanyong Zhang, Daniel Cremers, Yan Xia. 2025-11-27. ArtiBench and ArtiBrain: Benchmarking Generalizable Vision-Language Articulated Object Manipulation. https://arxiv.org/abs/2511.20330

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