Unitree G1 locomanipulation

Whole-body control tasks for Unitree G1 humanoid robot

This benchmark tests loco-manipulation capabilities on the Unitree G1 humanoid robot using whole-body control in MuJoCo. Tasks require navigation, object manipulation, and maintaining balance across the entire body.

Benchmark results

Checkpoint: nvidia/GR00T-N1.6-G1-PnPAppleToPlate

Task	Success rate
PnPAppleToPlate	58%

This task exhibits high evaluation variance. Fluctuations of ±15% are expected across different evaluation runs.

Fine-tuning

You can skip this section and directly evaluate with the provided checkpoint above.

1. Clone dataset repository

   Clone without downloading all files:

   cd examples/GR00T-WholeBodyControl
   
   git clone --filter=blob:none --no-checkout \
       https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
   
   cd PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
   
2. Setup sparse checkout

   Download only the G1 dataset:

   # Initialize sparse-checkout
   git sparse-checkout init --cone
   
   # Select folder to download
   git sparse-checkout set unitree_g1.LMPnPAppleToPlateDC
   
   # Checkout files
   git checkout
   
   # Pull LFS files
   git lfs pull
   
3. Run fine-tuning

   cd ../../../
   uv run bash examples/GR00T-WholeBodyControl/finetune_g1.sh
   

Evaluation

Setup environment

Install the required dependencies (only needs to be done once):

apt-get update
apt-get install libegl1-mesa-dev libglu1-mesa
bash gr00t/eval/sim/GR00T-WholeBodyControl/setup_GR00T_WholeBodyControl.sh

Run evaluation

1. Start policy server

   In Terminal 1, choose one of the following options:

   Option 1: Local fine-tuned checkpoint

   uv run python gr00t/eval/run_gr00t_server.py \
       --model-path /tmp/g1_finetune/checkpoint-10000/ \
       --embodiment-tag UNITREE_G1 \
       --use-sim-policy-wrapper
   

   Option 2: Remote fine-tuned checkpoint

   uv run python gr00t/eval/run_gr00t_server.py \
       --model-path nvidia/GR00T-N1.6-G1-PnPAppleToPlate \
       --embodiment-tag UNITREE_G1 \
       --use-sim-policy-wrapper
   
2. Start evaluation client

   In Terminal 2:

   gr00t/eval/sim/GR00T-WholeBodyControl/GR00T-WholeBodyControl_uv/.venv/bin/python \
       gr00t/eval/rollout_policy.py \
       --n_episodes 10 \
       --max_episode_steps=1440 \
       --env_name gr00tlocomanip_g1_sim/LMPnPAppleToPlateDC_G1_gear_wbc \
       --n_action_steps 20 \
       --n_envs 5
   

Available tasks

• gr00tlocomanip_g1_sim/LMPnPAppleToPlateDC_G1_gear_wbc - Pick apple and place on plate with dynamic constraints

Real robot deployment

When working with real Unitree G1 robot data, you have two options:

Option 1: Using GR00T-WholeBodyControl

If you collected data using GR00T-WholeBodyControl, leverage the UNITREE_G1 embodiment tag. This is a pre-trained embodiment with models already trained on in-the-wild Unitree G1 datasets.

Option 2: Custom whole-body controller

If your data was collected using a different whole-body controller, create and fine-tune with a NEW_EMBODIMENT tag. This allows you to define a custom embodiment tailored to your specific controller setup.

See the fine-tune new embodiment guide for detailed instructions.