{"ID":2880790,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13998","arxiv_id":"2508.13998","title":"Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation","abstract":"Generalization in embodied AI is hindered by the \"seeing-to-doing gap,\" which stems from data scarcity and embodiment heterogeneity. To address this, we pioneer \"pointing\" as a unified, embodiment-agnostic intermediate representation, defining four core embodied pointing abilities that bridge high-level vision-language comprehension with low-level action primitives. We introduce Embodied-R1, a 3B Vision-Language Model (VLM) specifically designed for embodied reasoning and pointing. We use a wide range of embodied and general visual reasoning datasets as sources to construct a large-scale dataset, Embodied-Points-200K, which supports key embodied pointing capabilities. We then train Embodied-R1 using a two-stage Reinforced Fine-tuning (RFT) curriculum with a specialized multi-task reward design. Embodied-R1 achieves state-of-the-art performance on 11 embodied spatial and pointing benchmarks. Critically, it demonstrates robust zero-shot generalization by achieving a 56.2% success rate in the SIMPLEREnv and 87.5% across 8 real-world XArm tasks without any task-specific fine-tuning, representing a 62% improvement over strong baselines. Furthermore, the model exhibits high robustness against diverse visual disturbances. Our work shows that a pointing-centric representation, combined with an RFT training paradigm, offers an effective and generalizable pathway to closing the perception-action gap in robotics.","short_abstract":"Generalization in embodied AI is hindered by the \"seeing-to-doing gap,\" which stems from data scarcity and embodiment heterogeneity. To address this, we pioneer \"pointing\" as a unified, embodiment-agnostic intermediate representation, defining four core embodied pointing abilities that bridge high-level vision-language...","url_abs":"https://arxiv.org/abs/2508.13998","url_pdf":"https://arxiv.org/pdf/2508.13998v2","authors":"[\"Yifu Yuan\",\"Haiqin Cui\",\"Yaoting Huang\",\"Yibin Chen\",\"Fei Ni\",\"Zibin Dong\",\"Pengyi Li\",\"Yan Zheng\",\"Hongyao Tang\",\"Jianye Hao\"]","published":"2025-08-19T16:50:01Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Language Model\"]","has_code":false}