--- license: gemma library_name: transformers base_model: - Dexmal/DM05 datasets: - Dexmal/robotwin2-full tags: - robotics - robot-control - vision-language-action - vla - dm05 - dm0.5 - robotwin - robotwin2 - opendm --- # DM05-robotwin2 ![DM0.5](https://raw.githubusercontent.com/dexmal/opendm/main/docs/image/header.png)

Tech Blog GitHub RoboTwin 2.0 Dataset MaaS

## Introduction DM05-robotwin2 is the RoboTwin 2.0 fine-tuned generalist checkpoint of DM0.5, Dexmal's open-world Vision-Language-Action foundation model for embodied intelligence. DM0.5 uses a Gemma3 4B vision-language backbone with a 680M Action Expert to generate continuous robot actions, and is designed for natural-language manipulation, zero-shot generalization, efficient downstream fine-tuning, long-horizon historical context, robust policy behavior, and transfer across robot embodiments. This checkpoint controls the ALOHA bimanual embodiment using three RGB camera views and generates 14-dimensional joint-position action chunks. #### RoboTwin 2.0 Results | Method | Clean | Randomized | Average | | --- | ---: | ---: | ---: | | DM0.5 | 93.6 | 93.3 | 93.5 | ## Quick Start We recommend using Docker to set up the runtime environment first, which helps avoid version mismatches across CUDA, PyTorch, flash-attn, and other dependencies on the host machine. ### Requirements ```text System requirements: Ubuntu 20.04 / 22.04 NVIDIA GPU NVIDIA Driver Docker NVIDIA Container Toolkit Conda (optional, only required for local pip installation) Recommended GPUs: A100, H100, H20 32 GPUs are recommended for training, and 1 GPU is sufficient for deployment inference. ``` ### Docker Installation ```bash git clone https://github.com/dexmal/opendm.git cd opendm docker run -it --rm --gpus all --network host \ --name opendm \ --shm-size=16g \ -v "$PWD":/app/opendm \ -w /app/opendm \ dexmal/opendm:latest /bin/bash # Run from the OpenDM repository root inside the container. conda activate opendm pip install -e . ``` ### Local Installation ```bash conda create -n opendm python=3.10 -y conda activate opendm pip install torch torchvision \ --index-url https://download.pytorch.org/whl/cu128 pip install ninja packaging MAX_JOBS=2 pip install flash-attn --no-build-isolation # Enter the OpenDM repository root. cd opendm pip install -e . ``` ## RoboTwin 2.0 Testing Use the RoboTwin 2.0-specific experiment configuration when evaluating this checkpoint. The inference service and benchmark client run separately and communicate over HTTP. When possible, use one GPU for the inference service and another for benchmark evaluation. For the complete data preparation, training, inference, and evaluation workflow, see the [DM05 RoboTwin 2.0 Training and Evaluation Guide](https://github.com/dexmal/opendm/blob/main/docs/en/dm05_robotwin2.md). ### Start the RoboTwin 2.0 Inference Service Run this command from the OpenDM repository root. The checkpoint must contain the matching `norm_stats.json`. ```bash script/dm05_launcher.sh \ --exp playground/dm05_robotwin2.py \ --task inference \ --nproc_per_node 1 \ --model-config.model-name-or-path ./checkpoints/DM05-robotwin2-bf16 \ --model-config.chunk-size 50 \ --inference-config.output-action-dim 14 \ --inference-config.image-keys images_1 images_2 images_3 \ --inference-config.port 7891 ``` Keep the service running while executing the benchmark. ### Prepare the Benchmark Client ```bash git clone https://github.com/dexmal/dexbotic-benchmark.git cd dexbotic-benchmark git submodule update --init --recursive RoboTwin docker pull dexmal/dexbotic_benchmark ``` RoboTwin 2.0 also requires its assets, object-data texture library, and embodiment files. Follow the [RoboTwin installation guide](https://robotwin-platform.github.io/doc/usage/robotwin-install.html#4-download-assets-robotwin-od-texture-library-and-embodiments) to download them before starting evaluation. Edit `evaluation/configs/robotwin2/adjust_bottle.yaml` so that `base_url` points to the inference service: ```yaml # Basic experiment configuration (keep unchanged) policy_name: dexbotic task_name: adjust_bottle task_config: demo_clean ckpt_setting: dexbotic seed: 0 instruction_type: seen # Add Parameters You Need base_url: http://localhost:7891 output_dir: ./result_test/robotwin2_evaluation cameras: "head_camera_rgb,left_camera_rgb,right_camera_rgb" action_horizon: 50 action_mode: absolute ``` Important configuration fields: - `task_name`: one of the 50 RoboTwin 2.0 tasks. Each task is evaluated independently. - `task_config`: use `demo_clean` for the Clean setting and `demo_randomized` for the Randomized setting. - `base_url`: address of the running DM0.5 inference service. For a remote service, use `http://:7891`. - `cameras`: keep the head, left-wrist, and right-wrist camera order so it matches `images_1`, `images_2`, and `images_3` on the inference service. - `action_horizon`: number of actions executed per model request. It must match the model chunk size of 50. - `output_dir`: root directory for success-rate files and rollout videos. - `action_mode`: `absolute` for absolute joint positions, `relative` for relative joint positions The benchmark evaluates 100 episodes for the selected task and setting. To reproduce the aggregate Clean and Randomized scores, evaluate all 50 tasks under both settings and average their success rates. ### Run the RoboTwin 2.0 Benchmark From the `dexbotic-benchmark` repository root, set `ROBOTWIN_ASSETS` to the absolute path of the downloaded assets and run: ```bash ROBOTWIN_ASSETS=/absolute/path/to/robotwin/assets docker run --rm --gpus all --network host \ -v "$ROBOTWIN_ASSETS":"$ROBOTWIN_ASSETS" \ -v "$ROBOTWIN_ASSETS":/app/assets \ -v "$ROBOTWIN_ASSETS":/app/RoboTwin/assets \ -v "$PWD/evaluation":/app/evaluation \ -v "$PWD/scripts":/app/scripts \ -v "$PWD/result_temp":/app/result_temp \ -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,graphics \ dexmal/dexbotic_benchmark \ bash scripts/env_sh/robotwin2.sh \ evaluation/configs/robotwin2/adjust_bottle.yaml ``` Evaluation artifacts are written under the configured `output_dir`, grouped by task, setting, and timestamp: ```text //// ├── _result.txt └── *.mp4 ``` `_result.txt` contains the success rate for the selected task and setting. When video logging is enabled by the RoboTwin task configuration, rollout videos are saved in the same timestamp directory. ## Community and Support - Learn more about Dexmal products and model updates on the [Dexmal website](https://www.dexmal.com/). - If you encounter issues, please report them through [GitHub Issues](https://github.com/dexmal/opendm/issues). - For further discussion, scan the [WeChat QR code](https://raw.githubusercontent.com/dexmal/opendm/main/docs/image/wechat.jpeg) to contact us. We will continue to release more model weights, technical documentation, and examples. If this project is helpful to you, please consider giving us a star on GitHub [![GitHub](https://img.shields.io/github/stars/dexmal/opendm?color=5B5BD6)](https://github.com/dexmal/opendm). Your support helps us move forward. ## Citation ```bibtex @misc{dm05, title = {{DM0.5}: An Open-World Foundation Model for General-Purpose Embodied Intelligence}, author = {{Dexmal Team}}, month = {July}, year = {2026}, url = {https://www.dexmal.com/blog/dm0.5/index_en.html} } ```