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The evaluation server script (gr00t/eval/run_gr00t_server.py) runs a policy as a network server, allowing multiple clients to send observations and receive actions over TCP.

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Usage

python gr00t/eval/run_gr00t_server.py \
  --model-path checkpoints/checkpoint-5000 \
  --embodiment-tag GR1 \
  --device cuda \
  --host 0.0.0.0 \
  --port 5555

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Parameters

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Policy configuration

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model-path
str
default:"None"
Path to the model checkpoint directory for GR00T policy inference.Either model-path or dataset-path must be provided.
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embodiment-tag
EmbodimentTag
default:"NEW_EMBODIMENT"
Embodiment tag identifying the robot configuration. See embodiment tags.
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device
str
default:"cuda"
Device to run the model on: cuda, cuda:0, cuda:1, or cpu.

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Replay policy configuration

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dataset-path
str
default:"None"
Path to a dataset for replay policy (replays recorded trajectories instead of running inference).Use this for debugging or baseline comparisons.
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modality-config-path
str
default:"None"
Path to a JSON file containing modality configuration for replay policy.If not provided, uses default configuration from MODALITY_CONFIGS[embodiment_tag].
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execution-horizon
int
default:"None"
Policy execution horizon during inference. If specified, overrides the default horizon.

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Server configuration

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host
str
default:"0.0.0.0"
Host address for the server. Use 0.0.0.0 to accept connections from any network interface.
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port
int
default:"5555"
Port number for the server to listen on.
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strict
bool
default:"True"
Whether to enforce strict input and output validation. Recommended to keep enabled for production.
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use-sim-policy-wrapper
bool
default:"False"
Whether to wrap the policy with Gr00tSimPolicyWrapper for simulation-specific processing.Enable this when serving policies for simulation environments.

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Example workflows

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Serve a GR00T policy

python gr00t/eval/run_gr00t_server.py \
  --model-path checkpoints/checkpoint-5000 \
  --embodiment-tag GR1 \
  --device cuda:0 \
  --port 5555
Output: 
Starting GR00T inference server...
  Embodiment tag: GR1
  Model path: checkpoints/checkpoint-5000
  Device: cuda:0
  Host: 0.0.0.0
  Port: 5555
Server running on 0.0.0.0:5555

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Serve for simulation

python gr00t/eval/run_gr00t_server.py \
  --model-path checkpoints/gr1_sim_model \
  --embodiment-tag GR1 \
  --use-sim-policy-wrapper \
  --port 5555

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Serve a replay policy

python gr00t/eval/run_gr00t_server.py \
  --dataset-path datasets/recorded_demos \
  --embodiment-tag GR1 \
  --modality-config-path configs/gr1_modalities.json \
  --execution-horizon 16

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Multi-GPU setup

Run multiple servers on different GPUs: 
# GPU 0
python gr00t/eval/run_gr00t_server.py \
  --model-path checkpoints/checkpoint-5000 \
  --device cuda:0 \
  --port 5555

# GPU 1
python gr00t/eval/run_gr00t_server.py \
  --model-path checkpoints/checkpoint-5000 \
  --device cuda:1 \
  --port 5556

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Client usage

Connect to the server using PolicyClient: 
from gr00t.policy.server_client import PolicyClient

# Create client
policy = PolicyClient(host="127.0.0.1", port=5555)

# Get action
observation = {
    "video": {"camera_0": image_array},
    "state": {"joint_positions": joint_array},
    "language": {"instruction": [["pick up the cup"]]}
}
action, metadata = policy.get_action(observation)

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With open-loop evaluation

# Terminal 1: Start server
python gr00t/eval/run_gr00t_server.py \
  --model-path checkpoints/checkpoint-5000 \
  --port 5555

# Terminal 2: Run open-loop eval
python gr00t/eval/open_loop_eval.py \
  --host 127.0.0.1 \
  --port 5555 \
  --dataset-path demo_data/cube_to_bowl_5/

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With closed-loop evaluation

# Terminal 1: Start server
python gr00t/eval/run_gr00t_server.py \
  --model-path checkpoints/checkpoint-5000 \
  --use-sim-policy-wrapper \
  --port 5555

# Terminal 2: Run closed-loop eval
python gr00t/eval/rollout_policy.py \
  --env-name gr1_unified/PnPCanToDrawerClose_GR1ArmsAndWaistFourierHands_Env \
  --policy-client-host 127.0.0.1 \
  --policy-client-port 5555 \
  --n-episodes 50

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Policy types

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Gr00tPolicy

Loaded when model-path is provided:
  • Runs transformer-based policy inference
  • Supports all GR00T embodiments
  • GPU-accelerated
  • Configurable via embodiment-tag and device

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ReplayPolicy

Loaded when dataset-path is provided:
  • Replays actions from recorded demonstrations
  • Useful for testing data collection pipelines
  • Configurable via modality-config-path and execution-horizon

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Server protocol

The server implements a simple request-response protocol:
  1. Client sends observation dictionary
  2. Server runs policy inference
  3. Server returns action dictionary and metadata
The PolicyServer class handles:
  • TCP socket management
  • Request serialization/deserialization
  • Multi-client support (sequential processing)
  • Graceful shutdown on KeyboardInterrupt

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Performance considerations

  • Single client per server: The server processes requests sequentially. For parallel inference, run multiple servers on different ports/GPUs.
  • Batch size: Each request processes a single observation. For higher throughput, use local policy inference instead.
  • Network latency: Server adds network overhead. For real-time applications, consider deploying on the same machine as the client.
The server does NOT authenticate clients. Only run on trusted networks or add authentication if deploying in production.

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Troubleshooting

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Port already in use

Address already in use: 0.0.0.0:5555
Change the port: 
python gr00t/eval/run_gr00t_server.py --port 5556

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Model path not found

FileNotFoundError: Model path checkpoints/checkpoint-5000 does not exist
Verify the checkpoint directory exists and contains model files.

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CUDA out of memory

RuntimeError: CUDA out of memory
Reduce batch size, use a smaller model, or switch to CPU: 
python gr00t/eval/run_gr00t_server.py --device cpu

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