This repository provides the codes used to train a phantomx robot to walk on rough terrain using NVIDIA's Isaac Gym. It includes the scripts used for training as well as those used for evaluation of the obtained policies.
Maintainer: Tania Sofía Ferreyra
Affiliation: Laboratorio de Robótica y Sistemas Embebidos, Instituto de Ciencias de la Computación, UBA-CONICET
Contact: tferreyra@icc.fcen.uba.ar
- Create a new python virtual env with python 3.10.
- Install Isaac Gym and Isaac Lab following the documentation's instructions. Make sure to include the installation of rsl_rl.
- Clone this repository.
- Install python extension hexapod_extension using pip.
- Train:
python hexapod-locomotion-isaac-sim/scripts/train.py --task=Isaac-Velocity-Rough-Phantom-X-v0- To run headless add
--headless. - To save videos of the training add
--video. - The trained policy is saved in
hexapod-locomotion-isaac-sim/logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt. Where<experiment_name>and<run_name>are defined in the train config. - You can override values set int he configuration files by adding any of the following line arguments:
- --task TASK_NAME: changes the task.
- --resume: resumes training from a given checkpoint.
- --experiment_name EXPERIMENT_NAME: name of the experiment to run or load.
- --num_envs NUM_ENVS: number of environments to use.
- --seed SEED: random seed.
- --max_iterations MAX_ITERATIONS: maximum number of training iterations.
- To run headless add
- Play a trained policy:
python hexapod-locomotion-isaac-sim/scripts/play.py --task=Isaac-Velocity-Rough-Phantom-X-Play-v0- By default, the loaded policy is the last model of the last run of the experiment folder.