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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import numpy as np
from gr00t.eval.robot import RobotInferenceServer
from gr00t.eval.simulation import (
MultiStepConfig,
SimulationConfig,
SimulationInferenceClient,
VideoConfig,
)
from gr00t.model.policy import Gr00tPolicy
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_path",
type=str,
help="Path to the model checkpoint directory.",
default="<PATH_TO_YOUR_MODEL>", # change this to your model path
)
parser.add_argument(
"--embodiment_tag",
type=str,
help="The embodiment tag for the model.",
default="<EMBODIMENT_TAG>", # change this to your embodiment tag
)
parser.add_argument(
"--env_name",
type=str,
help="Name of the environment to run.",
default="<ENV_NAME>", # change this to your environment name
)
parser.add_argument("--port", type=int, help="Port number for the server.", default=5555)
parser.add_argument(
"--host", type=str, help="Host address for the server.", default="localhost"
)
parser.add_argument("--video_dir", type=str, help="Directory to save videos.", default=None)
parser.add_argument("--n_episodes", type=int, help="Number of episodes to run.", default=2)
parser.add_argument("--n_envs", type=int, help="Number of parallel environments.", default=1)
parser.add_argument(
"--n_action_steps",
type=int,
help="Number of action steps per environment step.",
default=16,
)
parser.add_argument(
"--max_episode_steps", type=int, help="Maximum number of steps per episode.", default=1440
)
# server mode
parser.add_argument("--server", action="store_true", help="Run the server.")
# client mode
parser.add_argument("--client", action="store_true", help="Run the client")
args = parser.parse_args()
if args.server:
# Create a policy
policy = Gr00tPolicy(
model_path=args.model_path,
embodiment_tag=args.embodiment_tag,
)
# Start the server
server = RobotInferenceServer(policy, port=args.port)
server.run()
elif args.client:
# Create a simulation client
simulation_client = SimulationInferenceClient(host=args.host, port=args.port)
print("Available modality configs:")
modality_config = simulation_client.get_modality_config()
print(modality_config.keys())
# Create simulation configuration
config = SimulationConfig(
env_name=args.env_name,
n_episodes=args.n_episodes,
n_envs=args.n_envs,
video=VideoConfig(video_dir=args.video_dir),
multistep=MultiStepConfig(
n_action_steps=args.n_action_steps, max_episode_steps=args.max_episode_steps
),
)
# Run the simulation
print(f"Running simulation for {args.env_name}...")
env_name, episode_successes = simulation_client.run_simulation(config)
# Print results
print(f"Results for {env_name}:")
print(f"Success rate: {np.mean(episode_successes):.2f}")
else:
raise ValueError("Please specify either --server or --client")
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