Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914580940193792 |
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| author | Wang, Hanqing Wang, Shaoyang Zhong, Yiming Yang, Zemin Wang, Jiamin Cui, Zhiqing Yuan, Jiahao Han, Yifan Liu, Mingyu Ma, Yuexin |
| author_facet | Wang, Hanqing Wang, Shaoyang Zhong, Yiming Yang, Zemin Wang, Jiamin Cui, Zhiqing Yuan, Jiahao Han, Yifan Liu, Mingyu Ma, Yuexin |
| contents | Affordance grounding focuses on predicting the specific regions of objects that are associated with the actions to be performed by robots. It plays a vital role in the fields of human-robot interaction, human-object interaction, embodied manipulation, and embodied perception. Existing models often neglect the affordance shared among different objects because they lack the Chain-of-Thought(CoT) reasoning abilities, limiting their out-of-domain (OOD) generalization and explicit reasoning capabilities. To address these challenges, we propose Affordance-R1, the first unified affordance grounding framework that integrates cognitive CoT guided Group Relative Policy Optimization (GRPO) within a reinforcement learning paradigm. Specifically, we designed a sophisticated affordance function, which contains format, perception, and cognition rewards to effectively guide optimization directions. Furthermore, we constructed a high-quality affordance-centric reasoning dataset, ReasonAff, to support training. Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Affordance-R1 achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities. Comprehensive experiments demonstrate that our model outperforms well-established methods and exhibits open-world generalization. To the best of our knowledge, Affordance-R1 is the first to integrate GRPO-based RL with reasoning into affordance reasoning. The code of our method and our dataset is released on https://github.com/hq-King/Affordance-R1. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_06206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model Wang, Hanqing Wang, Shaoyang Zhong, Yiming Yang, Zemin Wang, Jiamin Cui, Zhiqing Yuan, Jiahao Han, Yifan Liu, Mingyu Ma, Yuexin Robotics Computer Vision and Pattern Recognition Affordance grounding focuses on predicting the specific regions of objects that are associated with the actions to be performed by robots. It plays a vital role in the fields of human-robot interaction, human-object interaction, embodied manipulation, and embodied perception. Existing models often neglect the affordance shared among different objects because they lack the Chain-of-Thought(CoT) reasoning abilities, limiting their out-of-domain (OOD) generalization and explicit reasoning capabilities. To address these challenges, we propose Affordance-R1, the first unified affordance grounding framework that integrates cognitive CoT guided Group Relative Policy Optimization (GRPO) within a reinforcement learning paradigm. Specifically, we designed a sophisticated affordance function, which contains format, perception, and cognition rewards to effectively guide optimization directions. Furthermore, we constructed a high-quality affordance-centric reasoning dataset, ReasonAff, to support training. Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Affordance-R1 achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities. Comprehensive experiments demonstrate that our model outperforms well-established methods and exhibits open-world generalization. To the best of our knowledge, Affordance-R1 is the first to integrate GRPO-based RL with reasoning into affordance reasoning. The code of our method and our dataset is released on https://github.com/hq-King/Affordance-R1. |
| title | Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.06206 |