stand-and-walk-locomotion_baseline's Introduction

Locomotion Baseline

Installation

conda create -n (your env name) python=3.8
conda activate (your env name)
cd
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
git clone [email protected]:inFpZero/Locomotion_Baseline.git
cd Locomotion_Baseline
# Download the Isaac Gym binaries from https://developer.nvidia.com/isaac-gym 
# Originally trained with Preview3, but haven't seen bugs using Preview4.
cd isaacgym/python && pip install -e .
cd /rsl_rl && pip install -e .
cd /legged_gym_train && pip install -e .
pip install "numpy<1.24" pydelatin swanlab tqdm opencv-python ipdb pyfqmr flask

Usage

cd legged_gym/scripts

Train base policy:

python train.py --exptid xxx-xx-WHATEVER --device cuda:0

Train 10-15k iterations (8-10 hours on 3090) (at least 15k recommended).

Play base policy:

python play.py --exptid xxx-xx

No need to write the full exptid. The parser will auto match runs with first 6 strings (xxx-xx). So better make sure you don't reuse xxx-xx.

Viewer Usage

Can be used in both IsaacGym and web viewer.

ALT + Mouse Left + Drag Mouse: move view.
[ ]: switch to next/prev robot.
Space: pause/unpause.
F: switch between free camera and following camera.

Arguments

--exptid: string, can be xxx-xx-WHATEVER, xxx-xx is typically numbers only. WHATEVER is the description of the run.
--device: can be cuda:0, cpu, etc.
--checkpoint: the specific checkpoint you want to load. If not specified load the latest one.
--resume: resume from another checkpoint, used together with --resumeid.
--seed: random seed.
--no_swanlab: no swanlab logging.
--debug: debug mode.
--web: used for playing on headless machines. It will forward a port with vscode and you can visualize seemlessly in vscode with your idle gpu or cpu. Live Preview vscode extension required, otherwise you can view it in any browser.