Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation
This work addresses the 'seeing-to-doing gap' for robotics and embodied AI, offering a generalizable solution to improve manipulation tasks, though it appears incremental in its approach to bridging perception and action.
The paper tackles the generalization problem in embodied AI by introducing 'pointing' as a unified intermediate representation and Embodied-R1, a 3B Vision-Language Model, which achieves state-of-the-art performance on 11 benchmarks and shows robust zero-shot generalization with a 56.2% success rate in SIMPLEREnv and 87.5% across 8 real-world tasks, representing a 62% improvement over baselines.
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.