AffordanceGrasp-R1:Leveraging Reasoning-Based Affordance Segmentation with Reinforcement Learning for Robotic Grasping

• URL: http://arxiv.org/abs/2602.03547v1
• Date: Tue, 03 Feb 2026 14:00:56 GMT
• Title: AffordanceGrasp-R1:Leveraging Reasoning-Based Affordance Segmentation with Reinforcement Learning for Robotic Grasping
• Abstract summary: AffordanceGrasp-R1 is a reasoning-driven affordance segmentation framework for robotic grasping.<n>We show that AffordanceGrasp-R1 consistently outperforms state-of-the-art (SOTA) methods on benchmark datasets.
• Score: 37.71157510922818
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce AffordanceGrasp-R1, a reasoning-driven affordance segmentation framework for robotic grasping that combines a chain-of-thought (CoT) cold-start strategy with reinforcement learning to enhance deduction and spatial grounding. In addition, we redesign the grasping pipeline to be more context-aware by generating grasp candidates from the global scene point cloud and subsequently filtering them using instruction-conditioned affordance masks. Extensive experiments demonstrate that AffordanceGrasp-R1 consistently outperforms state-of-the-art (SOTA) methods on benchmark datasets, and real-world robotic grasping evaluations further validate its robustness and generalization under complex language-conditioned manipulation scenarios.

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