Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models
Authors: name: Yifu Yuan, name: Yaoting Huang, name: Xianze Yao, name: Yutong Li, name: Shuoheng Zhang, name: Linqi Han, name: Pengyi Li, name: Jiangeng Sun, name: Wenting Jia, name: Zhao Zhang, name: Yuhao Liu, name: Ruihao Liao, name: Yucheng Hu, name: Qiyu Wu, name: Yuxiao Li, name: Zibin Dong, name: Fei Ni, name: Yan Zheng, name: Shuyang Gu, name: Yi Ma, name: Hongyao Tang, name: Han Hu, name: Jianye Hao

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.