YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
GR00T N1.7 Unitree G1 SONIC Deploy Checkpoint
This is the deploy-only export of:
checkpoints/gr00t_n17_g1_sonic/grab_bottle_v2_4gpu_bs64_10k/checkpoint-10000
Resume-training artifacts such as optimizer state, RNG state, scheduler state, trainer state, and training-only configs are intentionally not included here.
Included Files
config.json
model.safetensors.index.json
model-00001-of-00002.safetensors
model-00002-of-00002.safetensors
processor_config.json
statistics.json
embodiment_id.json
experiment_cfg/final_model_config.json
experiment_cfg/final_processor_config.json
experiment_cfg/dataset_statistics.json
Training Summary
- Base model:
nvidia/GR00T-N1.7-3B - Embodiment:
unitree_g1_sonic/UNITREE_G1_SONIC - Dataset:
cloudwalk-research/gr00t-g1-grab-bottle-right-hand-v2 - Task: Unitree G1 right-hand bottle grasp
- Max steps: 10,000
- Precision: bf16
- Action keys:
motion_token,left_hand_joints,right_hand_joints
Evaluation Summary
Open-loop checkpoint-10000 result:
- Avg MSE:
0.00029590 - Avg MAE:
0.01039191
MuJoCo / SONIC simulation result with lower body locked:
- Single bottle, grab bottle: 5 / 10 success
- Bottle + red apple, grab bottle: 3 / 10 success
- Bottle + red apple, grab red apple: 1 / 10 success
- Aggregate: 9 / 30 success
Real Unitree G1 deployment was not performed. The reported deployment success rates are simulation results only.
Usage
Load this directory as the GR00T policy checkpoint for SONIC VLA inference:
python gr00t/eval/service.py \
--model-path /path/to/deploy_checkpoint_10000 \
--embodiment-tag UNITREE_G1_SONIC \
--server
- Downloads last month
- 35
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support