Embodied-R1: Reinforced Embodied Reasoning for General Robotic
  Manipulation
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.
68a58dd638160bfd39426a85
Yifu Yuan
admin_assigned
2025-08-20T19:40:29.884Z
6695eb6c0a6ba12a31c3a1be
https://cdn-avatars.huggingface.co/v1/production/uploads/6695eb6c0a6ba12a31c3a1be/g5t82nhTIXYe92mKL535o.jpeg
Yifu Yuan
user
IffYuan
68a58dd638160bfd39426a86
Haiqin Cui
68a58dd638160bfd39426a87
Yaoting Huang
68a58dd638160bfd39426a88
Yibin Chen
68a58dd638160bfd39426a89
Fei Ni
68a58dd638160bfd39426a8a
Zibin Dong
68a58dd638160bfd39426a8b
Pengyi Li
claimed_verified
2025-09-24T13:57:48.942Z
643030c6ff56d6c20047a24d
/avatars/a3c605c6ed78b86bc83e59107c9998b9.svg
Pengyi Li
user
yeshenpy
68a58dd638160bfd39426a8c
Yan Zheng
68a58dd638160bfd39426a8d
Jianye Hao
Embodied-R1: Reinforced Embodied Reasoning for General Robotic
  Manipulation
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.
A pointing-centric representation and reinforced fine-tuning approach improve embodied AI generalization across various tasks and visual disturbances.
Vision-Language Model embodied reasoning pointing Embodied-R1 Embodied-Points-200K Reinforced Fine-tuning RFT embodied spatial benchmarks zero-shot generalization SIMPLEREnv XArm tasks robustness visual disturbances