Embodied-R1: Reinforced Embodied Reasoning for General Robotic
  Manipulation
Authors: name: Yifu Yuan, name: Haiqin Cui, name: Yaoting Huang, name: Yibin Chen, name: Fei Ni, name: Zibin Dong, name: Pengyi Li, name: Yan Zheng, name: Jianye Hao

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.