Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models
For embodied AI researchers, this work provides a unified model with strong performance across diverse embodied tasks, though it is an incremental improvement over existing foundation models.
Embodied-R1.5 is an 8B-parameter Embodied Foundation Model that integrates reasoning, planning, correction, and pointing capabilities. It achieves SOTA on 16/24 embodied VLM benchmarks, outperforming Gemini-Robotics-ER-1.5 and GPT-5.4, and can be fine-tuned into a VLA that surpasses π0.5 on 4 manipulation benchmarks.
We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, correction, and pointing, within a single architecture toward general physical intelligence. Leveraging three automated data construction pipelines to significantly expand the data coverage of critical capabilities, we build a large-scale data system of over 15B tokens, and design a multi-task balanced RL recipe to alleviate heterogeneous task conflicts. We further introduce a Planner-Grounder-Corrector (PGC) closed-loop framework that enables a single model to autonomously execute and self-correct over long-horizon tasks. With only 8B parameters, Embodied-R1.5 achieves SOTA on 16 out of 24 embodied VLM benchmarks, surpassing leading models like Gemini-Robotics-ER-1.5 and GPT-5.4. Benefiting from the internalized embodied capabilities, Embodied-R1.5 can be fine-tuned into a VLA with only a small amount of data, outperforming leading VLA models like $π_{0.5}$ across 4 popular manipulation benchmark suites. We further conduct extensive zero-shot real-robot experiments, validating performance in instruction following, affordance grounding, articulated object manipulation, and long-horizon complex tasks, demonstrating strong generalization to the physical world. We open-source model weights, datasets, training code, and EmbodiedEvalKit, an evaluation framework tailored for embodied tasks, to facilitate future research in EFMs.