Embodied-Omni

A series of open-source embodied-AI projects from ZJU-OmniAI — models that observe, reason, and act in the physical world.

Long-horizon task planning · environment-state reasoning · self-reflection · real-robot deployment

Embodied-Reasoner · Embodied-Navigator · EmbodiedMemory-Bench · Quick start · Citation

[!NOTE] This repository was renamed. It was previously zwq2018/embodied_reasoner — the URL cited in the Embodied-Reasoner paper — and is now ZJU-OmniAI/Embodied-Omni, hosting the whole Embodied-Omni series. GitHub redirects the old URL here automatically, so existing links, clones, and remotes keep working. Looking for Embodied-Reasoner? It now lives in embodied_reasoner/.

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Project	Resource	Type	All-time downloads
Embodied-Reasoner	embodied_reasoner	Dataset
Embodied-Navigator	Embodied-Navigator-7B-GRPO	Model
EmbodiedMemory-Bench	EmbodiedMemoryBench	Dataset

🌏 Overview

Embodied intelligence needs more than a strong VLM: an agent has to keep interacting with the world, remember what it has already seen, reason about where things are, and correct itself when a plan fails. Embodied-Omni collects our work on exactly this loop, and releases it end to end — data engines, training recipes, evaluation harnesses, and real-robot deployment code.

Three projects live here today, covering complementary parts of physical-world competence:

Project	One-line summary	Embodiment & benchmark	Status
🔍 Embodied-Reasoner	An embodied reasoning model for the physical world: it plans long-horizon tasks, reasons about the state of its environment, and reflects on its own actions while it keeps interacting.	Indoor agent in AI2-THOR (107 scenes)	ACL 2026 Main ✅
🧭 Embodied-Navigator	A vision-language navigation framework: the VLM points at a pixel instead of regressing coordinates, thinks only at critical nodes, compresses history into anchors, and is aligned with Two-Level GRPO.	Habitat R2R-CE / RxR-CE + Unitree Go2 quadruped
🧠 EmbodiedMemory-Bench	A benchmark for embodied memory: agents must retain observations, update world state, learn from interaction outcomes, and transfer experience across later tasks.	AI2-THOR & ProcTHOR · 2,554 episodes

Common design principles across the series:

• 🧠 Reason where it matters — explicit thinking (analysis, spatial reasoning, reflection, planning, verification) instead of blind action prediction.
• 🖼️ Interleaved multimodal context — long observation–thought–action trajectories rather than single-image QA.
• 🔁 Iterative alignment — imitation learning first, then self-exploration / reinforcement learning against environment feedback.
• 📦 Fully open — synthesis engines, datasets, training scripts, evaluation code, and deployment services, not just weights.

🔍 Embodied-Reasoner

🎬 Demo

https://github.com/user-attachments/assets/da9c5b42-ab8e-4101-9ec0-a226590d23fc

Embodied-Reasoner explores a room, reflects on what it has already checked, and finds hidden objects to complete a long-horizon instruction.

What it does

Embodied-Reasoner is an embodied reasoning model: given an instruction such as "Locate the apple and put it in the microwave", it sees only ego-centric images and alternates between thinking and acting for dozens of turns until the physical goal is reached.

• 👉 Long-horizon task planning — decomposes an instruction into a sequence of navigation and manipulation steps, and keeps that plan coherent across a whole episode.
• 👉 Environment-state reasoning — tracks where it has been, what each container holds, and where a still-unseen object is most likely to be, from a long interleaved image–text history.
• 👉 Reflection on its own behaviour — notices details it missed, rules out places it has already checked, and revises the plan instead of repeating a failed search.
• 👉 Interactive exploration — autonomously observes, navigates, opens containers, and manipulates or transports objects that are not visible at the start.
• 👉 Task and Trajectory Engine — automatically synthesizes coherent Observation–Thought–Action trajectories over 107 indoor scenes, 2,100 interactive objects, and 2,600 containers.
• 👉 Open data — 9.3k trajectories, 64k first-person images, and 8M thought tokens on Hugging Face.

Training is a three-stage recipe — imitation learning → self-exploration tuning → self-correction (reflection) tuning — producing the Embodied-Interactor / Explorer / Reasoner checkpoints in 7B and 2B sizes.

➡️ Full README, benchmarks, training, evaluation, and data engine →

🧭 Embodied-Navigator

🎬 Demo

https://github.com/user-attachments/assets/695b83b4-7672-4d77-ac5b-dc455258e036

Method overview and zero-shot deployment on a Unitree Go2 quadruped.

What it does

Instead of asking a VLM to regress 3D coordinates or emit long strings of atomic actions, Embodied-Navigator lets it act as a visual pointer: pick one of four RGB views, point at a pixel, and let a SLAM controller execute the projected 3D waypoint.

The policy is trained on R2R-CE and RxR-CE in Habitat and deployed zero-shot on a Unitree Go2 quadruped — no robot-specific fine-tuning.

📺 Real-world deployment videos

Six representative zero-shot trials, drawn from the 100-episode real-world evaluation (click to play on GitHub, or watch them inline on the project page):

➡️ Full README, benchmarks, training, evaluation, and robot serving →

🧠 EmbodiedMemory-Bench

🎬 Demo

https://github.com/user-attachments/assets/13a1025d-c691-4b15-892d-ecc5544418c4

EmbodiedMemory-Bench evaluates whether an agent can retain, update, and use embodied memory across long-horizon interactive tasks.

EmbodiedMemory-Bench evaluates whether an agent can build, maintain, and use memory while interacting with a changing environment. It contains 2,554 executable episodes across four challenges: passive observation, dynamic tracking, interaction-derived state, and experience generalization.

The release includes a simulator-grounded construction pipeline, a transparent Embodied-Memorizer baseline, and an evaluation runner. The complete benchmark data is available on Hugging Face, while this repository contains the public implementation and reproducible interfaces.

➡️ Full README, benchmark format, memory system, evaluation, and data access →

📂 Repository layout

Each project is self-contained in its own top-level directory, with its own README, requirements, and scripts.

Embodied-Omni/
├── embodied_reasoner/        # ACL 2026 · interactive embodied reasoning
│   ├── data_engine/          # Task & O1-style trajectory synthesis (AI2-THOR)
│   ├── data/                 # Dataset format and samples
│   ├── finetune/             # Three-stage training configs
│   ├── evaluate/             # 809-case interactive evaluation framework
│   ├── inference/            # Model inference
│   └── scripts/              # train.sh / eval.sh
├── embodied_navigator/       # Vision-language navigation
│   ├── src/agent/            # Policy, selective reasoning, Anchor-Trajectory Memory
│   ├── src/model/            # Navigation-adapted Qwen2.5-VL
│   ├── src/train/            # SFT + Two-Level GRPO
│   ├── src/env/ src/eval/    # Continuous-navigation env, NE/OS/SR/SPL/nDTW metrics
│   ├── src/server/           # FastAPI + ROS2 service for the Unitree Go2
│   ├── config/ scripts/      # Experiment configs and launch scripts
│   └── docs/                 # Project homepage, figures, deployment videos
├── embodied_memory/          # Embodied memory benchmark and memory system
│   ├── src/                  # EMem-Bench and Embodied-Memorizer packages
│   ├── assets/               # Paper and project figures
│   └── pyproject.toml        # Package and optional dependencies
└── assets/                   # Shared repository assets

⚡ Quick start

git clone https://github.com/ZJU-OmniAI/Embodied-Omni.git
cd Embodied-Omni

# Interactive embodied reasoning (AI2-THOR)
cd embodied_reasoner && cat README.md

# Vision-language navigation (Habitat / real robot)
cd ../embodied_navigator && cat README.md

# Embodied memory benchmark (AI2-THOR / ProcTHOR)
cd ../embodied_memory && cat README.md

Each project ships its own environment setup; do not mix them in one conda environment. Paths inside a project README are relative to that project directory.

📰 News

• 2026.09 — EmbodiedMemory-Bench joins the repository with 2,554 episodes, the Embodied-Memorizer baseline, and its public evaluation code.
• 2026.08 — Embodied-Navigator joins the repository; zwq2018/embodied_reasoner becomes ZJU-OmniAI/Embodied-Omni, the home of the whole series.
• 2026.04 — Embodied-Reasoner accepted to ACL 2026 Main Conference.
• 2026.01 — Invited talk @ 视觉语言导航.
• 2025.05 — Invited talk @ 智猩猩.
• 2025.03 — Embodied-Reasoner paper and dataset released.

📚 Citation

If you find this series useful, please cite the corresponding paper.

@inproceedings{zhang-etal-2026-embodied,
    title = "Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks",
    author = "Zhang, Wenqi  and
      Wang, Mengna  and
      Liu, Gangao  and
      Xu, Huixin  and
      Jiang, Yiwei  and
      Shen, Yongliang  and
      Hou, Guiyang  and
      Zheng, Zhe  and
      Zhang, Hang  and
      Li, Xin  and
      Liu, Jiajun  and
      Lu, Weiming  and
      Li, Peng  and
      Zhuang, Yueting",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.1910/",
    doi = "10.18653/v1/2026.acl-long.1910",
    pages = "41178--41207",
    ISBN = "979-8-89176-390-6"
}

@misc{feng2026embodiednavigator,
  title     = {Embodied-Navigator: Point, Think, Memorize, and Align
               for Efficient Embodied Navigation},
  author    = {Feng, Hongyan and Chen, Sunlai and Liu, Xuanyu and Pan, Miao and
               Xie, Yangfan and Cui, Yuxiang and Zhou, Zhongxiang and
               Xiong, Rong and Zhang, Wenqi and Yin, Jianwei and
               Zhuang, Yueting and Zhang, Xuhong},
  year      = {2026},
  eprint    = {2608.17512},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2608.17512}
}

@misc{embodiedmemorybench,
  title         = {EmbodiedMemory-Bench: Benchmarking Embodied Memory for Long-Horizon Embodied Tasks},
  author        = {Liang, Lizhou and Zhong, Xinyu and Pan, Miao and Zhou, Xiaohe and Liu, Xuanyu and Li, Qinfeng and Li, Peng and Chen, Jintao and Zhang, Xuhong and Zhang, Wenqi},
  year          = {2026},
  eprint        = {2609.28236},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2609.28236}
}

🙏 Acknowledgements

Embodied-Reasoner builds on AI2-THOR for simulation and LLaMA-Factory for training. Embodied-Navigator builds on Habitat-Lab and Qwen2.5-VL. EmbodiedMemory-Bench builds on AI2-THOR and ProcTHOR for simulator-grounded benchmark construction. Thanks to all of these projects.

📬 Contact

Questions, issues, and collaborations are welcome — open an issue or email us:

• Embodied-Reasoner — zhangwenqi@zju.edu.cn · lipeng@iscas.ac.cn
• Embodied-Navigator — hongyanfeng@zju.edu.cn · zhangxuhong@zju.edu.cn

📄 License

Released under the Mulan PSL v2 license.