Installation

1. Generate a new Python virtual environment with Python 3.8 using conda create -n isaacgym python=3.8,then conda activate isaacgym
2. For the best performance, we recommend using NVIDIA driver version 535 sudo apt install nvidia-driver-535. The minimal driver version supported is 515. If you're unable to install version 535, ensure that your system has at least version 515 to maintain basic functionality.
3. Install PyTorch 1.13 with Cuda-11.7:
   • conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
4. Install numpy-1.23 with conda install numpy=1.23.
5. Install Isaac Gym:
   • Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym.
   • cd isaacgym/python && pip install -e .
   • Run an example with cd examples && python 1080_balls_of_solitude.py.
   • Consult isaacgym/docs/index.html for troubleshooting.
6. Install ti5_isaacgym:
   • Clone this repository.
   • cd ti5_isaacgym && pip install -e .

Usage Guide

Examples

# Launching PPO Policy Training for 'ti5' Across 8192 Environments
# This command initiates the PPO algorithm-based training for the humanoid task.
python scripts/train.py --task=t1_dh_stand --run_name ti5 --headless --num_envs 8192

# Evaluating the Trained PPO Policy 'ti5'
# This command loads the 'ti5' policy for performance assessment in its environment. 
# Additionally, it automatically exports a JIT model, suitable for deployment purposes.
python scripts/play.py --task=t1_dh_stand --run_name ti5

# Implementing Simulation-to-Simulation Model Transformation
# This command facilitates a sim-to-sim transformation using exported 'ti5' policy.
python scripts/sim2sim.py --load_model /path/to/logs/t1_dh_stand/exported/policies/policy_1.pt

# Run our trained policy
python scripts/sim2sim.py --load_model /path/to/logs/t1_dh_stand/exported/policies/policy_example.pt

1. Default Tasks

• humanoid_ppo

  • Purpose: Baseline, PPO policy, Multi-frame low-level control
  • Observation Space: Variable $(47 \times H)$ dimensions, where $H$ is the number of frames
  • $[O_{t-H} ... O_t]$
  • Privileged Information: $73$ dimensions

• humanoid_dwl (coming soon)

2. PPO Policy

• Training Command: For training the PPO policy, execute:

  python humanoid/scripts/train.py --task=t1_dh_stand --load_run log_file_path --name run_name
  

• Running a Trained Policy: To deploy a trained PPO policy, use:

  python humanoid/scripts/play.py --task=t1_dh_stand --load_run log_file_path --name run_name
  

• By default, the latest model of the last run from the experiment folder is loaded. However, other run iterations/models can be selected by adjusting load_run and checkpoint in the training config.

3. Sim-to-sim

• Mujoco-based Sim2Sim Deployment: Utilize Mujoco for executing simulation-to-simulation (sim2sim) deployments with the command below:

  python scripts/sim2sim.py --load_model /path/to/export/model.pt
  

4. Parameters

• CPU and GPU Usage: To run simulations on the CPU, set both --sim_device=cpu and --rl_device=cpu. For GPU operations, specify --sim_device=cuda:{0,1,2...} and --rl_device={0,1,2...} accordingly. Please note that CUDA_VISIBLE_DEVICES is not applicable, and it's essential to match the --sim_device and --rl_device settings.
• Headless Operation: Include --headless for operations without rendering.
• Rendering Control: Press 'v' to toggle rendering during training.
• Policy Location: Trained policies are saved in humanoid/logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt.

5. Command-Line Arguments

For RL training, please refer to humanoid/utils/helpers.py#L161. For the sim-to-sim process, please refer to humanoid/scripts/sim2sim.py#L169.

Code Structure

1. Every environment hinges on an env file (legged_robot.py) and a configuration file (legged_robot_config.py). The latter houses two classes: LeggedRobotCfg (encompassing all environmental parameters) and LeggedRobotCfgPPO (denoting all training parameters).
2. Both env and config classes use inheritance.
3. Non-zero reward scales specified in cfg contribute a function of the corresponding name to the sum-total reward.
4. Tasks must be registered with task_registry.register(name, EnvClass, EnvConfig, TrainConfig). Registration may occur within envs/__init__.py, or outside of this repository.

Add a new environment

The base environment legged_robot constructs a rough terrain locomotion task. The corresponding configuration does not specify a robot asset (URDF/ MJCF) and no reward scales.

1. If you need to add a new environment, create a new folder in the envs/ directory with a configuration file named <your_env>_config.py. The new configuration should inherit from existing environment configurations.
2. If proposing a new robot:
   • Insert the corresponding assets in the resources/ folder.
   • In the cfg file, set the path to the asset, define body names, default_joint_positions, and PD gains. Specify the desired train_cfg and the environment's name (python class).
   • In the train_cfg, set the experiment_name and run_name.
3. If needed, create your environment in <your_env>.py. Inherit from existing environments, override desired functions and/or add your reward functions.
4. Register your environment in humanoid/envs/__init__.py.
5. Modify or tune other parameters in your cfg or cfg_train as per requirements. To remove the reward, set its scale to zero. Avoid modifying the parameters of other environments!