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Conditional Adversarial Latent Models

Code accompanying the paper: "CALM: Conditional Adversarial Latent Models for Directable Virtual Characters"
Skills

CALM builds upon, and borrows code from, Adversarial Skill Embeddings (Peng et. al., 2022, ASE).

Installation

Download Isaac Gym from the website, then follow the installation instructions.

Once Isaac Gym is installed, install the external dependencies for this repo:

pip install -r requirements.txt

CALM

Pre-Training

First, a CALM model can be trained to imitate a dataset of motions clips using the following command:

python calm/run.py --task HumanoidAMPGetup --cfg_env calm/data/cfg/humanoid_calm_sword_shield_getup.yaml --cfg_train calm/data/cfg/train/rlg/calm_humanoid.yaml --motion_file calm/data/motions/reallusion_sword_shield/dataset_reallusion_sword_shield.yaml --headless  --track

--motion_file can be used to specify a dataset of motion clips that the model should imitate. The task HumanoidAMPGetup will train a model to imitate a dataset of motion clips and get up after falling. Over the course of training, the latest checkpoint Humanoid.pth will be regularly saved to output/, along with a Tensorboard log. --headless is used to disable visualizations and --track is used for tracking using weights and biases. If you want to view the simulation, simply remove this flag. To test a trained model, use the following command:

python calm/run.py --test --task HumanoidAMPGetup --num_envs 16 --cfg_env calm/data/cfg/humanoid_calm_sword_shield_getup.yaml --cfg_train calm/data/cfg/train/rlg/calm_humanoid.yaml --motion_file calm/data/motions/reallusion_sword_shield/dataset_reallusion_sword_shield.yaml --checkpoint [path_to_calm_checkpoint]

You can also test the robustness of the model with --task HumanoidPerturb, which will throw projectiles at the character.

 

Precision-Training

After the CALM low-level controller has been trained, it can be used to train style-constrained-locomotion controllers. The following command will use a pre-trained CALM model to perform a target heading task:

python calm/run.py --task HumanoidHeadingConditioned --cfg_env calm/data/cfg/humanoid_sword_shield_heading_conditioned.yaml --cfg_train calm/data/cfg/train/rlg/hrl_humanoid_style_control.yaml --motion_file calm/data/motions/reallusion_sword_shield/dataset_reallusion_sword_shield_fsm_movements.yaml --llc_checkpoint [path_to_llc_checkpoint] --headless --track

--llc_checkpoint specifies the checkpoint to use for the low-level controller. A pre-trained CALM low-level controller is available in calm/data/models/calm_llc_reallusion_sword_shield.pth.

To test a trained model, use the following command:

python calm/run.py --test --task HumanoidHeadingConditioned --num_envs 16 --cfg_env calm/data/cfg/humanoid_sword_shield_heading_conditioned.yaml --cfg_train calm/data/cfg/train/rlg/hrl_humanoid.yaml --motion_file calm/data/motions/reallusion_sword_shield/dataset_reallusion_sword_shield_fsm_movements.yaml --llc_checkpoint [path_to_llc_checkpoint] --checkpoint [path_to_hlc_checkpoint]

 

Task-Solving (Inference -- no training!)

The CALM low-level controller and the high-level locomotion controller can be combined to solve tasks without further trianing. This phase is inference only.

python calm/run.py --test --task HumanoidStrikeFSM --num_envs 16 --cfg_env calm/data/cfg/humanoid_sword_shield_strike_fsm.yaml --cfg_train calm/data/cfg/train/rlg/hrl_humanoid_fsm.yaml --motion_file calm/data/motions/reallusion_sword_shield/dataset_reallusion_sword_shield_fsm_movements.yaml --llc_checkpoint [path_to_llc_checkpoint] --checkpoint [path_to_hlc_checkpoint]

--llc_checkpoint specifies the checkpoint to use for the low-level controller. A pre-trained CALM low-level controller is available in calm/data/models/calm_llc_reallusion_sword_shield.pth. --checkpoint specified the checkpoint to use for the precision-trained high-level controller. A pre-trained high-level precision-trained controller is available in calm/data/models/calm_hlc_precision_trained_reallusion_sword_shield.pth.

The built-in tasks and their respective config files are:

HumanoidStrikeFSM: calm/data/cfg/humanoid_sword_shield_strike_fsm.yaml
HumanoidLocationFSM: calm/data/cfg/humanoid_sword_shield_location_fsm.yaml

 

 

Task-Training

In addition to precision training, a high-level controller can also be trained to directly solve tasks. The following command will use a pre-trained CALM model to perform a target heading task:

python calm/run.py --task HumanoidHeading --cfg_env calm/data/cfg/humanoid_sword_shield_heading.yaml --cfg_train calm/data/cfg/train/rlg/hrl_humanoid.yaml --motion_file calm/data/motions/reallusion_sword_shield/RL_Avatar_Idle_Ready_Motion.npy --llc_checkpoint [path_to_llc_checkpoint] --headless --track

--llc_checkpoint specifies the checkpoint to use for the low-level controller. A pre-trained CALM low-level controller is available in calm/data/models/calm_llc_reallusion_sword_shield.ckpt. --task specifies the task that the character should perform, and --cfg_env specifies the environment configurations for that task. The built-in tasks and their respective config files are:

HumanoidReach: calm/data/cfg/humanoid_sword_shield_reach.yaml
HumanoidHeading: calm/data/cfg/humanoid_sword_shield_heading.yaml
HumanoidLocation: calm/data/cfg/humanoid_sword_shield_location.yaml
HumanoidStrike: calm/data/cfg/humanoid_sword_shield_strike.yaml

To test a trained model, use the following command:

python calm/run.py --test --task HumanoidHeading --num_envs 16 --cfg_env calm/data/cfg/humanoid_sword_shield_heading.yaml --cfg_train calm/data/cfg/train/rlg/hrl_humanoid.yaml --motion_file calm/data/motions/reallusion_sword_shield/RL_Avatar_Idle_Ready_Motion.npy --llc_checkpoint [path_to_llc_checkpoint] --checkpoint [path_to_hlc_checkpoint]

 

 

AMP

We also provide an implementation of Adversarial Motion Priors (https://xbpeng.github.io/projects/AMP/index.html). A model can be trained to imitate a given reference motion using the following command:

python calm/run.py --task HumanoidAMP --cfg_env calm/data/cfg/humanoid_sword_shield.yaml --cfg_train calm/data/cfg/train/rlg/amp_humanoid.yaml --motion_file calm/data/motions/reallusion_sword_shield/sword_shield/RL_Avatar_Atk_2xCombo01_Motion.npy --headless  --track

The trained model can then be tested with:

python calm/run.py --test --task HumanoidAMP --num_envs 16 --cfg_env calm/data/cfg/humanoid_sword_shield.yaml --cfg_train calm/data/cfg/train/rlg/amp_humanoid.yaml --motion_file calm/data/motions/reallusion_sword_shield/sword_shield/RL_Avatar_Atk_2xCombo01_Motion.npy --checkpoint [path_to_amp_checkpoint]

 

 

Motion Data

Motion clips are located in calm/data/motions/. Individual motion clips are stored as .npy files. Motion datasets are specified by .yaml files, which contains a list of motion clips to be included in the dataset. Motion clips can be visualized with the following command:

python calm/run.py --test --task HumanoidViewMotion --num_envs 2 --cfg_env calm/data/cfg/humanoid_sword_shield.yaml --cfg_train calm/data/cfg/train/rlg/amp_humanoid.yaml --motion_file calm/data/motions/reallusion_sword_shield/sword_shield/RL_Avatar_Atk_2xCombo01_Motion.npy

--motion_file can be used to visualize a single motion clip .npy or a motion dataset .yaml.

If you want to retarget new motion clips to the character, you can take a look at an example retargeting script in calm/poselib/retarget_motion.py.