ASAP: Aligning Simulation and Real-World Physics for

Learning Agile Humanoid Whole-Body Skills

[Website] [Arxiv] [Video]

                   

TODO

• Release code backbone
• Release phase-based motion tracking training pipeline
• Release ASAP motion datasets
• Release motion retargeting pipeline
• Release sim2sim in MuJoCo
• Release sim2real with UnitreeSDK
• Release ASAP delta action model training pipeline

Installation

ASAP codebase is built on top of HumanoidVerse (a multi-simulator framework for humanoid learning) and Human2Humanoid (our prior work on humanoid whole-body tracking).

HumanoidVerse allows you to train humanoid skills in multiple simulators, including IsaacGym, IsaacSim, and Genesis. Its key design logic is the separation and modularization of simulators, tasks, and algorithms, which enables smooth transfers between different simulators and the real world with minimum effort (just one line of code change). We leverage this framework to develop ASAP and study how to best transfer policies across simulators and the real world.

IsaacGym Conda Env

Create mamba/conda environment, in the following we use conda for example, but you can use mamba as well.

conda create -n hvgym python=3.8
conda activate hvgym

Install IsaacGym

Download IsaacGym and extract:

wget https://developer.nvidia.com/isaac-gym-preview-4
tar -xvzf isaac-gym-preview-4

Install IsaacGym Python API:

pip install -e isaacgym/python

Test installation:

python 1080_balls_of_solitude.py  # or
python joint_monkey.py

For libpython error:

• Check conda path:

  conda info -e

• Set LD_LIBRARY_PATH:

  export LD_LIBRARY_PATH=</path/to/conda/envs/your_env/lib>:$LD_LIBRARY_PATH

Install HumanoidVerse

Install dependencies:

pip install -e .
pip install -e isaac_utils

Test with:

HYDRA_FULL_ERROR=1 python humanoidverse/train_agent.py \
+simulator=isaacgym \
+exp=locomotion \
+domain_rand=NO_domain_rand \
+rewards=loco/reward_g1_locomotion \
+robot=g1/g1_29dof_anneal_23dof \
+terrain=terrain_locomotion_plane \
+obs=loco/leggedloco_obs_singlestep_withlinvel \
num_envs=1 \
project_name=TestIsaacGymInstallation \
experiment_name=G123dof_loco \
headless=False

Note:

This is ONLY for testing, NOT how we train the locomotion policy in the ASAP paper. But still, you can train a locomotion policy by:

HYDRA_FULL_ERROR=1 python humanoidverse/train_agent.py \
+simulator=isaacgym \
+exp=locomotion \
+domain_rand=NO_domain_rand \
+rewards=loco/reward_g1_locomotion \
+robot=g1/g1_29dof_anneal_23dof \
+terrain=terrain_locomotion_plane \
+obs=loco/leggedloco_obs_singlestep_withlinvel \
num_envs=4096 \
project_name=TestIsaacGymInstallation \
experiment_name=G123dof_loco \
headless=True \
rewards.reward_penalty_curriculum=True \
rewards.reward_initial_penalty_scale=0.1 \
rewards.reward_penalty_degree=0.00003 

IsaacLab Environment

Install IsaacSim

1. Download Omniverse Launcher
2. Install Isaac Sim through launcher
3. Set environment variables:

export ISAACSIM_PATH="${HOME}/.local/share/ov/pkg/isaac-sim-4.2.0"
export ISAACSIM_PYTHON_EXE="${ISAACSIM_PATH}/python.sh"

Install IsaacLab

git clone https://github.com/isaac-sim/IsaacLab.git
cd IsaacLab && ./isaaclab.sh --conda hvlab
mamba activate hvlab
sudo apt install cmake build-essential
./isaaclab.sh --install

Setup HumanoidVerse

pip install -e .
pip install -e isaac_utils

Genesis Environment

mamba create -n hvgen python=3.10
mamba activate hvgen
pip install genesis-world torch

Install dependencies:

pip install -e .
pip install -e isaac_utils

Motion Tracking Training

Train a phase-based motion tracking policy to imitate Cristiano Ronaldo's signature Siuuu move

python humanoidverse/train_agent.py \
+simulator=isaacgym \
+exp=motion_tracking \
+domain_rand=NO_domain_rand \
+rewards=motion_tracking/reward_motion_tracking_dm_2real \
+robot=g1/g1_29dof_anneal_23dof \
+terrain=terrain_locomotion_plane \
+obs=motion_tracking/deepmimic_a2c_nolinvel_LARGEnoise_history \
num_envs=4096 \
project_name=MotionTracking \
experiment_name=MotionTracking_CR7 \
robot.motion.motion_file="humanoidverse/data/motions/g1_29dof_anneal_23dof/TairanTestbed/singles/0-TairanTestbed_TairanTestbed_CR7_video_CR7_level1_filter_amass.pkl" \
rewards.reward_penalty_curriculum=True \
env.config.resample_motion_when_training=False

Citation

If you find our work useful, please consider citing us!

@article{he2025asap,
  title={ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills},
  author={He, Tairan and Gao, Jiawei and Xiao, Wenli and Zhang, Yuanhang and Wang, Zi and Wang, Jiashun and Luo, Zhengyi and He, Guanqi and Sobanbabu, Nikhil and Pan, Chaoyi and Yi, Zeji and Qu, Guannan and Kitani, Kris and Hodgins, Jessica and Fan, Linxi "Jim" and Zhu, Yuke and Liu, Changliu and Shi, Guanya},
  journal={arXiv preprint arXiv:2502.01143},
  year={2025}
}

License

This project is licensed under the MIT License - see the LICENSE file for details.