Description
Unitree G1
Humanoid agent AI avatar
Start the agent New era

G1 and G1 EDU Parameter Comparison Table
Parameter |
G1 |
G1 EDU |
---|---|---|
Mechanical Dimensions | ||
Height, Width, Thickness (Stand) | 1320x450x200 mm | 1320x450x200 mm |
Height, Width, Thickness (Fold) | 690x450x300 mm | 690x450x300 mm |
Weight (With Battery) | About 35 kg | About 35 kg+ |
Degrees of Freedom | ||
Total Joint Freedom | 23 | 23-43 |
Single Leg Degrees of Freedom | 6 | 6 |
Waist Degrees of Freedom | 1 | 1 (+Optional 2 Additional Degrees) |
Single Arm Degrees of Freedom | 5 | 5 |
Single Hand Degrees of Freedom | N/A | 7 (Optional Force-Controlled) |
Joint and Motion Features | ||
Joint Output Bearing | Industrial-grade crossed roller bearings (high precision, high load capacity) | Same as G1 |
Joint Motor | Low-inertia high-speed PMSM | Low-inertia high-speed PMSM |
Knee Joint Torque | 90 N.m | 120 N.m |
Arm Maximum Load | About 2 kg | About 3 kg |
Calf + Thigh Length | 0.6 m | 0.6 m |
Arm Span | About 0.45 m | About 0.45 m |
Extra Large Joint Movement Space | Waist: Z ±155°; Knee: 0-165°; Hip: P ±154°, R -30 to +170°, Y ±158° | Waist: Z ±155°, X ±45°, Y ±30°; Wrist: P ±92.5°, Y ±92.5° |
Electrical and Computing | ||
Full Joint Hollow Electrical Routing | YES | YES |
Joint Encoder | Dual encoder | Dual encoder |
Cooling System | Local air cooling | Local air cooling |
Power Supply | 13-string lithium battery | 13-string lithium battery |
Basic Computing Power | 8-core high-performance CPU | 8-core high-performance CPU |
Sensing Sensor | Depth Camera + 3D LiDAR | Depth Camera + 3D LiDAR |
4 Microphone Array | YES | YES |
Speaker | 5W | 5W |
Connectivity | WiFi 6 + Bluetooth 5.2 | WiFi 6 + Bluetooth 5.2 |
Accessories | ||
High Computing Power Module | N/A | NVIDIA Jetson Orin |
Smart Battery (Quick Release) | 9000mAh | 9000mAh |
Charger | 54V 5A | 54V 5A |
Manual Controller | YES | YES |
Other | ||
Battery Life | About 2 hours | About 2 hours |
Upgraded Intelligent OTA | YES | YES |
Secondary Development | N/A | YES |
Warranty Period | 8 months | 1 year |
Updated Usage and Safety Points
- Dynamic Configurations: The robot’s specifications may change depending on scenarios and configuration, ensuring adaptability for varied use cases. Always prioritize proper setup to optimize performance.
- Safety First: The Unitree G1 is designed with powerful motor systems and a sophisticated structure. Maintain a safe distance during operation and follow guidelines to ensure safety.
- Continuous Updates: Select features showcased are under development and will become accessible through future updates, ensuring your robot stays ahead of the curve.
- Innovative Civilian Use: The Unitree G1 is a highly advanced civilian-grade robot. To ensure its safe use, modifications that may compromise performance or create hazards should be avoided.
- Comprehensive Support: For detailed product information, guidelines, and compliance with local laws, please contact us.
Unitree RL GYM
This is a repository for reinforcement learning implementation based on Unitree robots, supporting Unitree Go2, H1, H1_2, and G1.
📦 Installation and Configuration
Please refer to setup.md for installation and configuration steps.
🔁 Process Overview
The basic workflow for using reinforcement learning to achieve motion control is:
Train
→Play
→Sim2Sim
→Sim2Real
- Train: Use the Gym simulation environment to let the robot interact with the environment and find a policy that maximizes the designed rewards. Real-time visualization during training is not recommended to avoid reduced efficiency.
- Play: Use the Play command to verify the trained policy and ensure it meets expectations.
- Sim2Sim: Deploy the Gym-trained policy to other simulators to ensure it’s not overly specific to Gym characteristics.
- Sim2Real: Deploy the policy to a physical robot to achieve motion control.
🛠️ User Guide
1. Training
Run the following command to start training:
python legged_gym/scripts/train.py --task=xxx
⚙️ Parameter Description
--task
: Required parameter; values can be (go2, g1, h1, h1_2).--headless
: Defaults to starting with a graphical interface; set to true for headless mode (higher efficiency).--resume
: Resume training from a checkpoint in the logs.--experiment_name
: Name of the experiment to run/load.--run_name
: Name of the run to execute/load.--load_run
: Name of the run to load; defaults to the latest run.--checkpoint
: Checkpoint number to load; defaults to the latest file.--num_envs
: Number of environments for parallel training.--seed
: Random seed.--max_iterations
: Maximum number of training iterations.--sim_device
: Simulation computation device; specify CPU as--sim_device=cpu
.--rl_device
: Reinforcement learning computation device; specify CPU as--rl_device=cpu
.
Default Training Result Directory:
logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt
2. Play
To visualize the training results in Gym, run the following command:
python legged_gym/scripts/play.py --task=xxx
Description:
- Play’s parameters are the same as Train’s.
- By default, it loads the latest model from the experiment folder’s last run.
- You can specify other models using
load_run
andcheckpoint
.
💾 Export Network
Play exports the Actor network, saving it in
logs/{experiment_name}/exported/policies
:- Standard networks (MLP) are exported as
policy_1.pt
. - RNN networks are exported as
policy_lstm_1.pt
.
Play Results
Go2 G1 H1 H1_2
3. Sim2Sim (Mujoco)
Run Sim2Sim in the Mujoco simulator:
python deploy/deploy_mujoco/deploy_mujoco.py {config_name}
Parameter Description
config_name
: Configuration file; default search path isdeploy/deploy_mujoco/configs/
.
Example: Running G1
python deploy/deploy_mujoco/deploy_mujoco.py g1.yaml
➡️ Replace Network Model
The default model is located at
deploy/pre_train/{robot}/motion.pt
; custom-trained models are saved inlogs/g1/exported/policies/policy_lstm_1.pt
. Update thepolicy_path
in the YAML configuration file accordingly.Simulation Results
G1 H1 H1_2
4. Sim2Real (Physical Deployment)
Before deploying to the physical robot, ensure it’s in debug mode. Detailed steps can be found in the Physical Deployment Guide:
python deploy/deploy_real/deploy_real.py {net_interface} {config_name}
Parameter Description
net_interface
: Network card name connected to the robot, e.g.,enp3s0
.config_name
: Configuration file located indeploy/deploy_real/configs/
, e.g.,g1.yaml
,h1.yaml
,h1_2.yaml
.
Deployment Results
G1 H1 H1_2 📂 Explore Catalog!
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