FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation

[Website] [Arxiv] [Video]

TODO

• Release training code
• Release sim2sim code
• Release sim2real code

Installation

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 fcgym python=3.8
conda activate fcgym

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:

cd isaacgym/python/examples

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 FALCON

git clone https://github.com/LeCAR-Lab/FALCON.git
cd FALCON

pip install -e .
pip install -e isaac_utils

FALCON Training

Unitree G1_29DoF

Training Command

python humanoidverse/train_agent.py \
+exp=decoupled_locomotion_stand_height_waist_wbc_diff_force_ma_ppo_ma_env \
+simulator=isaacgym \
+domain_rand=domain_rand_rl_gym \
+rewards=dec_loco/reward_dec_loco_stand_height_ma_diff_force \
+robot=g1/g1_29dof_waist_fakehand \
+terrain=terrain_locomotion_plane \
+obs=dec_loco/g1_29dof_obs_diff_force_history_wolinvel_ma \
num_envs=4096 \
project_name=g1_29dof_falcon \
experiment_name=g1_29dof_falcon \
+opt=wandb \
obs.add_noise=True \
env.config.fix_upper_body_prob=0.3 \
robot.dof_effort_limit_scale=0.9 \
rewards.reward_initial_penalty_scale=0.1 \
rewards.reward_penalty_degree=0.0001

Evaluation Command

python humanoidverse/eval_agent.py \
+checkpoint=<path_to_your_ckpt>

After around 6k iterations, in IsaacGym:

Booster T1_29DoF

Training Command

python humanoidverse/train_agent.py \
+exp=decoupled_locomotion_stand_height_waist_wbc_diff_force_ma_ppo_ma_env \
+simulator=isaacgym \
+domain_rand=domain_rand_rl_gym \
+rewards=dec_loco/reward_dec_loco_stand_height_ma_diff_force \
+robot=t1/t1_29dof_waist_wrist \
+terrain=terrain_locomotion_plane \
+obs=dec_loco/t1_29dof_obs_diff_force_history_wolinvel_ma \
num_envs=4096 \
project_name=t1_29dof_falcon \
experiment_name=t1_29dof_falcon \
+opt=wandb \
obs.add_noise=True \
env.config.fix_upper_body_prob=0.3 \
robot.dof_effort_limit_scale=0.9 \
rewards.reward_initial_penalty_scale=0.1 \
rewards.reward_penalty_degree=0.0001 \
rewards.feet_height_target=0.08 \
rewards.feet_height_stand=0.02 \
rewards.desired_feet_max_height_for_this_air=0.08 \
rewards.desired_base_height=0.62 \
rewards.reward_scales.penalty_lower_body_action_rate=-0.5 \
rewards.reward_scales.penalty_upper_body_action_rate=-0.5 \
env.config.apply_force_pos_ratio_range=[0.5,2.0]

Evaluation Command

python humanoidverse/eval_agent.py \
+checkpoint=<path_to_your_ckpt>

After around 6k iterations, in IsaacGym:

Citation

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

@article{zhang2025falcon,
          title={FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation},
          author={Zhang, Yuanhang and Yuan, Yifu and Gurunath, Prajwal and He, Tairan and Omidshafiei, Shayegan and Agha-mohammadi, Ali-akbar and Vazquez-Chanlatte, Marcell and Pedersen, Liam and Shi, Guanya},
          journal={arXiv preprint arXiv:2505.06776},
          year={2025}
        }

Acknowledgement

FALCON is built upon ASAP and HumanoidVerse.

License

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