🦿 Legged Gym#

Tip

For models and code, see the MotrixSim Docs repository.

Before running the examples, please refer to 🛠️ Environment Setup to complete the environment setup.

In the usage examples, we provide a simple framework similar to legged gym, making it convenient for users to test policies trained in legged gym (sim2sim) within MotrixSim.

For more information about the legged gym training framework, click here.

The following legged gym sim2sim examples are included in MotrixSim:

Description

Command

Effect

Go1 controlled movement

uv run legged_gym/scripts/go1_play.py

Go1

T1 Walking

uv run legged_gym/scripts/T1_play.py

T1

Berkeley Humanoid Lite Walking

Note

The walking policy is from the Berkeley-Humanoid-Lite repository.

uv run legged_gym/scripts/BHL_play.py

BHL

Dual-arm Berkeley Humanoid Lite Walking

Note

The walking policy is from the Berkeley-Humanoid-Lite repository.

uv run legged_gym/scripts/BHA_play.py

BHL

Custom Env#

The legged gym sim2sim framework we provide is designed to be as consistent as possible with legged gym’s Env design, minimizing the learning curve for sim2sim adaptation. The directory structure is as follows:

  • legged_gym

    • envs: Custom environments

    • policy: Policy files

    • resources: MJCF model files

    • scripts: Play scripts

    • utils: Utility functions

Taking T1 sim2sim as an example, we create a folder named T1 under envs, which contains two files: T1.py and T1_config.py, inheriting from legged_gym.envs.base.legged_robot.Legged_Robot and legged_gym.envs.base.legged_robot_config.LeggedRobotCfg, respectively. You can override the config or env to implement custom observation and action calculations.

The play script for T1 is defined in legged_gym/scripts/T1_play.py:

# Copyright (C) 2020-2025 Motphys Technology Co., Ltd. All Rights Reserved.
#
# 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.
# ==============================================================================

# from legged_gym.envs.base.legged_robot import Legged_Robot
import numpy as np
import onnxruntime as ort

from legged_gym.addr import LEGGED_GYM_ENVS_DIR
from legged_gym.envs.T1.T1 import T1_env
from legged_gym.envs.T1.T1_config import T1Cfg
from legged_gym.utils import runner

policy_path = "/policy/booster_t1/booster_t1_10000.onnx"
env = T1_env(T1Cfg)
session = ort.InferenceSession(LEGGED_GYM_ENVS_DIR + policy_path, providers=["CPUExecutionProvider"])
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
actions = np.zeros(12)


def step():
    global actions
    env.step(actions)
    obs = env.get_observation()
    # print(obs)
    input_data = obs.reshape(1, 47).astype(np.float32)
    outputs = session.run([output_name], {input_name: input_data})
    # Read actions from output
    actions = outputs[0][0]


runner.loop(policy_step=step, render=env.render, policy_dt=env.config.sim.dt * env.config.control.decimation)
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