Paper page - Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

Source: https://huggingface.co/papers/2606.11324 Authors:

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Abstract

Embodied-R1.5 is a unified embodied foundation model that integrates embodied reasoning capabilities and achieves state-of-the-art performance on embodied vision-language benchmarks through a multi-task balanced reinforcement learning approach.

We introduce Embodied-R1.5, a unifiedEmbodied Foundation Model(EFM) that integrates comprehensive embodied reasoning capabilities, spanningembodied cognition,task planning,correction, andpointing, within a single architecture toward general physical intelligence. Leveraging three automateddata construction pipelinesto significantly expand the data coverage of critical capabilities, we build a large-scale data system of over 15B tokens, and design amulti-task balanced RLrecipe 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 aVLAwith only a small amount of data, outperforming leadingVLAmodels 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.

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