Quick start

Get started quickly by downloading a pre-trained checkpoint and running inference on the GR1 embodiment.

Prerequisites

GR00T installed (see Installation)
CUDA-enabled GPU
Internet connection to download model checkpoint

Start the policy server

GR00T uses a server-client architecture for inference. Start the policy server with a pre-trained checkpoint:

uv run python gr00t/eval/run_gr00t_server.py \
  --embodiment-tag GR1 \
  --model-path nvidia/GR00T-N1.6-3B

The server will:

Download the nvidia/GR00T-N1.6-3B checkpoint from Hugging Face (if not already cached)
Load the model on GPU
Start listening on localhost:5555 for inference requests

On first run, the model checkpoint (approximately 6GB) will be downloaded from Hugging Face. Subsequent runs will use the cached version.

Run standalone inference

Run inference on a sample dataset to verify everything works:

uv run python scripts/deployment/standalone_inference_script.py \
  --model-path nvidia/GR00T-N1.6-3B \
  --dataset-path demo_data/gr1.PickNPlace \
  --embodiment-tag GR1 \
  --traj-ids 0 1 2 \
  --inference-mode pytorch \
  --action-horizon 8

This script:

Loads the GR00T N1.6 3B parameter model
Runs inference on trajectories 0, 1, and 2 from the demo dataset
Uses PyTorch mode (no TensorRT acceleration)
Predicts 8 future action steps per inference call

Expected output

You should see timing information similar to:

Data Processing: 2 ms
Backbone: 18-25 ms
Action Head: 16-38 ms
End-to-End: 37-58 ms
Frequency: 17-27 Hz

Actual timing depends on your GPU. See the performance table below.

Inference performance

GR00T N1.6 3B inference timing (4 denoising steps, single view):

Device	Mode	Data Processing	Backbone	Action Head	E2E	Frequency
RTX 5090	torch.compile	2 ms	18 ms	16 ms	37 ms	27.3 Hz
H100	torch.compile	4 ms	23 ms	11 ms	38 ms	26.3 Hz
RTX 4090	torch.compile	2 ms	25 ms	17 ms	44 ms	22.8 Hz
Thor	torch.compile	5 ms	39 ms	61 ms	105 ms	9.5 Hz

For 2x faster inference, see TensorRT optimization.

Run zero-shot evaluation

For a more complete example with simulation environments, try the RoboCasa GR1 tabletop tasks:

Start the policy server

uv run python gr00t/eval/run_gr00t_server.py \
  --embodiment-tag GR1 \
  --model-path nvidia/GR00T-N1.6-3B

Run evaluation

In a separate terminal, navigate to the RoboCasa example:

cd examples/robocasa-gr1-tabletop-tasks

Follow the instructions in examples/robocasa-gr1-tabletop-tasks/README.md for environment setup and evaluation.

Using the policy API

To integrate GR00T into your own environment, use the Policy API:

from gr00t.policy.server_client import PolicyClient

# Connect to the policy server
policy = PolicyClient(host="localhost", port=5555)

# Verify connection
if not policy.ping():
    raise RuntimeError("Cannot connect to policy server!")

# Create observation in the expected format
observation = {
    "video": {
        "camera_name": video_array,  # Shape: (B, T, H, W, 3), dtype: uint8
    },
    "state": {
        "state_name": state_array,   # Shape: (B, T, D), dtype: float32
    },
    "language": {
        "task": [["pick up the apple"]],  # Shape: (B, 1)
    }
}

# Get action from policy
action, info = policy.get_action(observation)

# Execute action in your environment
env.step(action)

For detailed information on observation/action formats and policy integration, see the Policy API guide.

Available pre-trained models

Base models

Model	Parameters	Use Case	Checkpoint
GR00T N1.6	3B	Finetuning	nvidia/GR00T-N1.6-3B
GR00T N1.5	3B	Finetuning	nvidia/GR00T-N1.5-3B

Finetuned models

Model	Embodiment	Description	Checkpoint
GR00T-N1.6-bridge	WidowX	Bridge dataset manipulation tasks	nvidia/GR00T-N1.6-bridge
GR00T-N1.6-fractal	Google Robot	Fractal dataset manipulation tasks	nvidia/GR00T-N1.6-fractal
GR00T-N1.6-BEHAVIOR1k	Galaxea R1 Pro	BEHAVIOR-1K loco-manipulation	nvidia/GR00T-N1.6-BEHAVIOR1k
GR00T-N1.6-G1-PnPAppleToPlate	Unitree G1	Loco-manipulation pick-and-place	nvidia/GR00T-N1.6-G1-PnPAppleToPlate
GR00T-N1.6-DROID	DROID	DROID manipulation tasks	nvidia/GR00T-N1.6-DROID

Next steps

Data preparation

Prepare your robot data for training

Finetuning

Finetune GR00T on your custom data

Policy API

Learn the Policy API for integration

Evaluation

Evaluate your trained models

Overview

Getting Started

Core Concepts

Guides

Benchmarks & Examples

Deployment

Resources

Prerequisites

Start the policy server

Run standalone inference

Expected output

Inference performance

Run zero-shot evaluation

Using the policy API

Available pre-trained models

Base models

Finetuned models

Next steps

Data preparation

Finetuning

Policy API

Evaluation

Build docs developers (and LLMs) love

Overview

Getting Started

Core Concepts

Guides

Benchmarks & Examples

Deployment

Resources

​Prerequisites

​Start the policy server

​Run standalone inference

​Expected output

​Inference performance

​Run zero-shot evaluation

​Using the policy API

​Available pre-trained models

​Base models

​Finetuned models

​Next steps

Data preparation

Finetuning

Policy API

Evaluation

Build docs developers (and LLMs) love

Prerequisites

Start the policy server

Run standalone inference

Expected output

Inference performance

Run zero-shot evaluation

Using the policy API

Available pre-trained models

Base models

Finetuned models

Next steps