Highlights

• Unified embodied capability system. A single 8B model unifies three capability dimensions: Cognition & Spatial Reasoning, Planning & Correction, and Pointing & Location.
• State-of-the-art performance. Achieves SOTA on 16 out of 24 embodied VLM benchmarks, with an average score of 70.4% across 21 main accuracy-based benchmarks, surpassing Gemini-Robotics-ER-1.5 and GPT-5.4 by 17.0% and 21.7% respectively.
• Closed-loop autonomy. The PGC framework lets one model serve as planner, grounder, and corrector simultaneously, completing long-horizon real-world tasks (e.g., making milk tea, sweeping garbage, stacking cups) without human intervention.
• Efficient adaptation to action. Because embodied reasoning is internalized upstream, the model can be fine-tuned into Embodied-R1.5-VLA with only a small amount of action data, outperforming strong VLA baselines such as $\pi 0.5$ across 4 popular manipulation benchmark suites (e.g., 92.4% on SimplerEnv Google Robot Visual Matching).
• Fully open-source. We release model weights, datasets, training code, and EmbodiedEvalKit, an evaluation framework tailored for embodied tasks.

Model Details

• Architecture: Qwen3-VL (Qwen3VLForConditionalGeneration)
• Parameters: ~8B
• Modality: Image / Video + Text → Text
• Output format: All outputs are plain-text token sequences. Coordinates are normalized to $[0,1000]$, trajectories are ordered coordinate sequences, and reasoning is free-form text. The final decision is emitted within an <answer>...</answer> tag.

Unified Capabilities

1. Embodied Cognition & Spatial Reasoning — comprehends the semantic and spatial structure of the physical world, including static geometric relations and dynamic interaction possibilities.
2. Embodied Planning & Correction — covers the full task life cycle: long-horizon task decomposition, next-step planning, process detection, error localization, and error correction.
3. Embodied Pointing & Location — grounds high-level reasoning in coordinates and trajectories, covering referring expression grounding, region-level localization, functional (affordance) grounding, and visual trace generation.

Quick Start

python
Copy code
from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image

model_id = "IffYuan/Embodied-R1.5"
model = AutoModelForImageTextToText.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

image = Image.open("scene.jpg")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "You are a robot performing manipulation tasks. "
                                     "The task instruction is: move the blue cube on top of the yellow cube. "
                                     "Use 2D points to mark the target location."},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
print(processor.batch_decode(out, skip_special_tokens=True)[0])

The model reasons over the visual observation and emits its final decision within an <answer> tag, e.g. <answer>[{"point_2d": [750, 748]}]</answer>.

Citation

bibtex
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@article{yuan2026embodiedr15,
  title   = {Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models},
  author  = {Yuan, Yifu and others},
  year    = {2026}
}

License

Released under the Apache 2.0 license.