--- title: G1 Moves emoji: "\U0001F916" colorFrom: blue colorTo: indigo sdk: static pinned: false license: cc-by-4.0 ---

Experiential Technologies

# G1 Moves > **[Dataset](https://huggingface.co/datasets/exptech/g1-moves)** (BVH, FBX, PKL, NPZ, ONNX policies) · **Showcase** (you are here) · **[Code](https://github.com/experientialtech/g1-moves)** (scripts & docs) 60 motion capture clips for the Unitree G1 humanoid robot (29 DOF, mode 15). Dance, karate, fencing, and more — captured with MOVIN TRACIN, retargeted with Motion-Player-ROS, trained with mjlab, and deployed via RoboJuDo. Browse the interactive gallery above to view trained RL policies running in real-time in your browser via MuJoCo WASM + ONNX Runtime. --- ## Pipeline ### 1. Capture Motion is captured with the [MOVIN TRACIN](https://movin3d.com/) markerless mocap system (LiDAR + vision, 60 FPS) and recorded in [MOVIN Studio](https://movin3d.com/). Output is BVH format with a 51-joint humanoid skeleton. Alternatively, motions can be extracted from any video (YouTube, phone, AI-generated) using [video2robot](https://github.com/AIM-Intelligence/video2robot), which runs PromptHMR pose extraction followed by [GMR](https://github.com/YanjieZe/GMR) inverse kinematics retargeting — no mocap hardware needed. ### 2. Retarget Each BVH clip is retargeted to the Unitree G1's 29-DOF joint space using per-frame inverse kinematics via [MOVIN-SDK-Python](https://github.com/MOVIN3D/MOVIN-SDK-Python) and a custom [ROS 2 retargeting node](https://github.com/MOVIN3D/Motion-Player-ROS). The node provides real-time preview with dual BVH skeleton + robot visualization in RViz, and supports both playback of pre-recorded `.pkl` files and live retargeting from MOVIN TRACIN data via OSC/UDP. ```bash # Playback mode ros2 launch motion_player player.launch.py \ motion_file:=clip.pkl bvh_file:=clip.bvh # Real-time mocap ros2 launch motion_player realtime.launch.py \ port:=9000 human_height:=1.75 ``` ### 3. Train Retargeted motions are trained as reinforcement learning policies using [mjlab](https://github.com/mujocolab/mjlab), which combines Isaac Lab's manager-based API with GPU-accelerated [MuJoCo-Warp](https://github.com/google-deepmind/mujoco_warp) simulation. PPO training runs across 8,192 parallel environments on a single NVIDIA RTX PRO 6000. - **Actor**: 4-layer MLP (160 → 512 → 256 → 128 → 29, ELU activations) - **Observation** (160-dim): reference joint pos/vel, 14-body anchor tracking, IMU, proprioception - **Control**: 50 Hz, adaptive early stopping when time-out ratio >= 0.95 - **Reward**: position, orientation, velocity, and collision avoidance shaping across 14 tracked bodies ```bash # Train a motion imitation policy uv run train Mjlab-Tracking-Flat-Unitree-G1 \ --env.commands.motion.motion-file clip.npz \ --env.scene.num-envs 8192 \ --agent.max-iterations 30000 ``` Every trained policy is exported as an ONNX model (160-dim input, 29-dim output) with baked-in observation normalization. ### 4. Deploy Policies are deployed to the physical G1 via [RoboJuDo](https://github.com/GDDG08/RoboJuDo), a plug-and-play sim2real framework with modular controller/environment/policy architecture. Supports seamless switching between MuJoCo sim2sim validation and real hardware deployment via Unitree SDK over Ethernet (192.168.123.x). Hardware E-stop and tethered first-run safety protocol required. ```bash # Sim2sim validation python scripts/run_pipeline.py --config=g1 # Deploy to physical G1 python scripts/run_pipeline.py --config=g1_real ``` ## License CC BY 4.0 — [Experiential Technologies](https://mitchchaiet.com/) Full observation vector spec and integration guide in the [dataset README](https://huggingface.co/datasets/exptech/g1-moves). --- ## Safety Disclaimer & Limitation of Liability **WARNING: Deploying learned locomotion policies to physical robots is inherently dangerous and can cause serious injury, death, or property damage.** The policies provided in this dataset are trained entirely in simulation and have **not** been validated for safe real-world operation. Sim-to-real transfer involves unpredictable failure modes including but not limited to: - Sudden loss of balance and uncontrolled falls - Uncontrolled high-speed or high-torque limb movements - Unexpected collisions with people, objects, or the environment - Hardware damage to actuators, joints, or structural components - Divergent or unstable behavior not observed in simulation - Failure to respond to emergency stop commands under certain conditions ### Requirements for Physical Deployment **Do not deploy these policies to a physical robot unless you have:** 1. **Advanced expertise** in sim-to-real transfer, robot safety systems, real-time control, and the specific robot platform (Unitree G1) 2. **Appropriate physical safety infrastructure** including overhead tethers or gantry systems, rigid safety enclosures, hardware emergency stop mechanisms independent of software, and adequate clearance from personnel and obstacles 3. **Completed thorough sim-to-sim validation** in a separate MuJoCo environment to verify policy behavior before any hardware deployment 4. **Conducted a formal risk assessment** for your specific deployment environment, considering all potential failure modes and their consequences 5. **Ensured all personnel** in the vicinity are trained on emergency procedures, aware of the risks, and positioned at a safe distance 6. **Verified insurance coverage** adequate for robotics research and development activities 7. **Compliance with all applicable local, state, and federal regulations** governing the operation of autonomous robotic systems ### Limitation of Liability THE POLICIES, MODELS, TRAINING DATA, SOFTWARE, ONNX MODELS, CONFIGURATION FILES, AND ALL OTHER MATERIALS IN THIS DATASET (COLLECTIVELY, THE "MATERIALS") ARE PROVIDED "AS IS" AND "AS AVAILABLE" WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, SAFETY, ACCURACY, RELIABILITY, NON-INFRINGEMENT, OR SUITABILITY FOR ANY PARTICULAR APPLICATION. IN NO EVENT SHALL EXPERIENTIAL TECHNOLOGIES, ITS AFFILIATES, OFFICERS, DIRECTORS, EMPLOYEES, AGENTS, CONTRIBUTORS, LICENSORS, OR ANY OTHER PARTY INVOLVED IN CREATING, PRODUCING, OR DELIVERING THE MATERIALS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, CONSEQUENTIAL, OR PUNITIVE DAMAGES OF ANY KIND (INCLUDING BUT NOT LIMITED TO PERSONAL INJURY, WRONGFUL DEATH, PROPERTY DAMAGE, LOSS OF USE, LOSS OF DATA, LOSS OF PROFITS, BUSINESS INTERRUPTION, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES, OR ANY OTHER COMMERCIAL OR NON-COMMERCIAL DAMAGES OR LOSSES) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, NEGLIGENCE, TORT, OR ANY OTHER LEGAL OR EQUITABLE THEORY, ARISING OUT OF OR IN ANY WAY RELATED TO THE USE, INABILITY TO USE, OR PERFORMANCE OF THE MATERIALS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES AND EVEN IF A REMEDY SET FORTH HEREIN IS FOUND TO HAVE FAILED OF ITS ESSENTIAL PURPOSE. ### Assumption of Risk and Indemnification By downloading, accessing, using, modifying, or deploying any part of the Materials, you expressly acknowledge and agree that: - You assume **all risk** and **full responsibility** for any and all consequences arising from the use of the Materials, including deployment to physical hardware - You have the **requisite technical expertise** to evaluate the safety and suitability of the Materials for your intended use - You will **indemnify, defend, and hold harmless** Experiential Technologies, its officers, directors, employees, agents, and affiliates from and against any and all claims, damages, losses, liabilities, costs, and expenses (including reasonable attorneys' fees) arising from or related to your use of the Materials - The Materials are intended for **research and educational purposes** and have not been certified or validated for any safety-critical application - You are solely responsible for ensuring compliance with all applicable laws, regulations, and safety standards in your jurisdiction This disclaimer applies to all uses of the Materials, whether in simulation, on physical hardware, or in any derivative works.