Obstruction reasoning for robotic grasping

• URL: http://arxiv.org/abs/2511.23186v1
• Date: Fri, 28 Nov 2025 13:53:12 GMT
• Title: Obstruction reasoning for robotic grasping
• Authors: Runyu Jiao, Matteo Bortolon, Francesco Giuliari, Alice Fasoli, Sergio Povoli, Guofeng Mei, Yiming Wang, Fabio Poiesi,
• Abstract summary: We present UNOGrasp, a learning-based vision-language model capable of performing obstruction reasoning.<n>We devise a novel multi-step reasoning process based on obstruction paths originated by the target object.<n>We construct UNOBench, a large-scale dataset for both training and benchmarking, based on MetaGraspNetV2.
• Score: 18.39507400925748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Successful robotic grasping in cluttered environments not only requires a model to visually ground a target object but also to reason about obstructions that must be cleared beforehand. While current vision-language embodied reasoning models show emergent spatial understanding, they remain limited in terms of obstruction reasoning and accessibility planning. To bridge this gap, we present UNOGrasp, a learning-based vision-language model capable of performing visually-grounded obstruction reasoning to infer the sequence of actions needed to unobstruct the path and grasp the target object. We devise a novel multi-step reasoning process based on obstruction paths originated by the target object. We anchor each reasoning step with obstruction-aware visual cues to incentivize reasoning capability. UNOGrasp combines supervised and reinforcement finetuning through verifiable reasoning rewards. Moreover, we construct UNOBench, a large-scale dataset for both training and benchmarking, based on MetaGraspNetV2, with over 100k obstruction paths annotated by humans with obstruction ratios, contact points, and natural-language instructions. Extensive experiments and real-robot evaluations show that UNOGrasp significantly improves obstruction reasoning and grasp success across both synthetic and real-world environments, outperforming generalist and proprietary alternatives. Project website: https://tev-fbk.github.io/UnoGrasp/.

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