/IsaacGym_legged_gym

Primary LanguagePythonOtherNOASSERTION

Method to Torque = 0, to mimic joint failure:

In the legged_gym > envs > anymal_c folder, there is anymal.py script. Within, this script, go to compute torque function and comment and uncomment lines before training to set the joints diabling.

Below is note from the legged_robot github

Isaac Gym Environments for Legged Robots

This repository provides the environment used to train ANYmal (and other robots) to walk on rough terrain using NVIDIA's Isaac Gym. It includes all components needed for sim-to-real transfer: actuator network, friction & mass randomization, noisy observations and random pushes during training.
Maintainer: Nikita Rudin
Affiliation: Robotic Systems Lab, ETH Zurich
Contact: rudinn@ethz.ch

Useful Links

Project website: https://leggedrobotics.github.io/legged_gym/ Paper: https://arxiv.org/abs/2109.11978

Installation

  1. Create a new python virtual env with python 3.6, 3.7 or 3.8 (3.8 recommended)
  2. Install pytorch 1.10 with cuda-11.3:
    • pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
  3. Install Isaac Gym
  • ./create_conda_env_rlgpu.sh and in terminal initiate env : conda activate rlgpu
  • Add following path to the ~/.bashrc file : LD_LIBRARY_PATH=/home/"User_name"/"anaconda_folder"/envs/rlgpu/lib:$LD_LIBRARY_PATH
    • Try running an example cd examples && python 1080_balls_of_solitude.py
    • For troubleshooting check docs isaacgym/docs/index.html)
  1. Install rsl_rl (PPO implementation)
  2. Install legged_gym
    • Clone this repository
    • cd legged_gym && pip install -e .
  3. Add following path to the ~/.bashrc file.
    • LD_LIBRARY_PATH=/home/"User_name"/"anaconda_folder"/envs/rlgpu/lib:$LD_LIBRARY_PATH

CODE STRUCTURE

  1. Each environment is defined by an env file (legged_robot.py) and a config file (legged_robot_config.py). The config file contains two classes: one conatianing all the environment parameters (LeggedRobotCfg) and one for the training parameters (LeggedRobotCfgPPo).
  2. Both env and config classes use inheritance.
  3. Each non-zero reward scale specified in cfg will add a function with a corresponding name to the list of elements which will be summed to get the total reward.
  4. Tasks must be registered using task_registry.register(name, EnvClass, EnvConfig, TrainConfig). This is done in envs/__init__.py, but can also be done from outside of this repository.

Usage

  1. Train:
    python legged_gym/scripts/train.py --task=anymal_c_flat
    • To run on CPU add following arguments: --sim_device=cpu, --rl_device=cpu (sim on CPU and rl on GPU is possible).
    • To run headless (no rendering) add --headless.
    • Important: To improve performance, once the training starts press v to stop the rendering. You can then enable it later to check the progress.
    • The trained policy is saved in issacgym_anymal/logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt. Where <experiment_name> and <run_name> are defined in the train config.
    • The following command line arguments override the values set in the config files:
    • --task TASK: Task name.
    • --resume: Resume training from a checkpoint
    • --experiment_name EXPERIMENT_NAME: Name of the experiment to run or load.
    • --run_name RUN_NAME: Name of the run.
    • --load_run LOAD_RUN: Name of the run to load when resume=True. If -1: will load the last run.
    • --checkpoint CHECKPOINT: Saved model checkpoint number. If -1: will load the last checkpoint.
    • --num_envs NUM_ENVS: Number of environments to create.
    • --seed SEED: Random seed.
    • --max_iterations MAX_ITERATIONS: Maximum number of training iterations.
  2. Play a trained policy:
    python issacgym_anymal/scripts/play.py --task=anymal_c_flat
    • By default the loaded policy is the last model of the last run of the experiment folder.
    • Other runs/model iteration can be selected by setting load_run and checkpoint in the train config.

Troubleshooting

  1. If you get the following error: ImportError: libpython3.8m.so.1.0: cannot open shared object file: No such file or directory, do: sudo apt install libpython3.8