RynnWorld-Teleop

<div align="center">

RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation

</div> <p align="center"> 💫 <a href="https://alibaba-damo-academy.github.io/RynnWorld-Teleop.github.io/"><b>Project Page</b></a>&nbsp;&nbsp; | &nbsp;&nbsp; 🤗 <a href ="https://huggingface.co/collections/Alibaba-DAMO-Academy/RynnWorld-Teleop"><b> Hugging Face </b></a> &nbsp;&nbsp; | &nbsp;&nbsp; 🤖 <a href = "https://www.modelscope.cn/collections/DAMO_Academy/RynnWorld-Teleop"><b> ModelScope</b></a> &nbsp;|&nbsp; 🚀 <a href="https://huggingface.co/spaces/Alibaba-DAMO-Academy/RynnWorld-Teleop"><b>Video</b></a> &nbsp;&nbsp; | &nbsp;&nbsp; 📄 <a href="https://arxiv.org/abs/2602.14979v1">arXiv</a>&nbsp;&nbsp; </p>

---

🌟 Abstract

We introduce RynnWorld-Teleop, a robot-centric generative world model that instantiates the paradigm of digital teleoperation—decoupling robot data collection from physical hardware constraints. By transforming an operator's real-time hand-pose stream into high-fidelity egocentric robotic videos from a single reference image, RynnWorld-Teleop enables the scaling of expert trajectories in a purely virtual environment. Our framework integrates depth-aware skeletal conditioning with a progressive human-to-robot training curriculum, allowing it to inherit rich manipulation priors from large-scale human datasets. To support interactive use, we distill the model into a causal, autoregressive student capable of real-time streaming. Policies trained exclusively on RynnWorld-Teleop synthetic data achieve effective zero-shot Sim2Real transfer, demonstrating its power as a high-fidelity data engine for scaling dexterous robotic learning.

---

📰 News

• [2026.07.07] 🔥🔥 Release our <a href="https://alibaba-damo-academy.github.io/RynnWorld-Teleop.github.io/assets/RynnWorld-Teleop_Report.pdf">Technical Report</a> !!
• [2026.07.07] 🔥🔥 Release our code and model checkpoints!!

---

📦 This Repository

This repository hosts the SFT (full fine-tune) checkpoint of RynnWorld-Teleop. Given a first-frame image and a hand-pose / skeleton control video, the model generates a high-fidelity egocentric robotic video.

Model Zoo

---

🚀 Quick Start

Please refer to the code repository for the full setup, training, and inference pipeline.

🔧 Environment Setup

conda create -n "rynnworld-teleop" python=3.10 -y
conda activate rynnworld-teleop
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

📖 Download the Checkpoint

Our model is developed on top of Wan2.2-TI2V-5B. Download the base model and place it under pretrained/:

RynnWorld-Teleop/
└── pretrained/
    └── Wan2.2-TI2V-5B-Diffusers/
        ├── model_index.json
        ├── scheduler/
        ├── transformer/
        ├── vae/
        └── ...

Then download our fine-tuned weights:

mkdir -p pretrained/RynnWorld-Teleop
huggingface-cli download Alibaba-DAMO-Academy/RynnWorld-Teleop --local-dir pretrained/RynnWorld-Teleop

---

🎬 Inference

Given a first-frame image and a control video (hand-pose / OpenPose mp4), the model generates the corresponding egocentric video.

python inference_user.py \
  --image <first_frame.png> \
  --control_video <control.mp4> \
  --output results/my_demo \
  --prompt "Describe the action in one sentence." \
  --checkpoint <sft_checkpoint_dir> \
  --mode sft \
  --control_type add \
  --seeds "42,123,7"

Required arguments

• --image: first-frame image (jpg/png), automatically resized to 832×480
• --control_video: control video mp4 (hand-pose / OpenPose), sampled/interpolated to 81 frames
• --output: output directory

Useful options

• --mode sft|lora: which checkpoint type to load (default: sft)
• --prompt: optional natural-language description (encoded with the T5 text encoder)
• --text_embedding: alternative pre-encoded prompt embedding .safetensors
• --seeds "42,123,7": generate multiple samples in one run
• --no_ema: use raw weights instead of EMA
• --guidance_scale: classifier-free guidance scale (default 1.0)
• --control_type add|concat|add-plus: how the control signal is merged

For real-time streaming inference with the distilled causal student, please see the RynnWorld-Teleop-Causal checkpoint.

---

🏋️ Training

We train the teacher model in three stages:

• Stage 0 — Pretrain (egocentric human videos): Full-parameter SFT on large-scale egocentric data, no control-video conditioning. Absorbs general manipulation priors.
• Stage 1 — Control-conditioned fine-tuning: Adds a zero-initialized control_patch_embedding (Conv3d) and a learnable control_scale to inject hand-pose control video into the diffusion process. LoRA (lightweight) and Full-SFT (best quality) variants are provided.
• Distillation → Causal student: Distilled into a causal autoregressive model for real-time streaming.

Full training scripts, configs, and data-preparation instructions are available in the code repository.

---

📑 Citation

If you find this project useful, please cite:

@article{rynnworld_teleop,
  title  = {RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation},
  author = {DAMO Academy, Alibaba Group},
  year   = {2026},
}

License

Apache License 2.0 — see LICENSE for details.