Basic Usage¶
This section provides a brief overview of the main PACE scripts and their most important command-line arguments. It is intended as a quick reference for running common workflows, without going into full implementation or theory details.
The typical workflow consists of:
- Collecting excitation data from the robot or simulation
- Running parameter identification using CMA-ES
- Visualising and analysing the resulting trajectories and scores
Overview of main scripts¶
| Script | Purpose |
|---|---|
data_collection.py |
Collects excitation data using a chirp signal for system identification |
fit.py |
Runs the PACE parameter identification using CMA-ES |
plot_trajectory.py |
Visualises the resulting trajectories and optimization progress |
1. Data collection¶
python scripts/pace/data_collection.py
This script generates excitation trajectories using a chirp signal and records the corresponding robot states for later parameter identification.
Common arguments¶
| Argument | Description |
|---|---|
--num_envs |
Number of parallel simulation environments |
--task |
Name of the task / robot configuration |
--min_frequency |
Minimum frequency of the chirp signal (Hz) |
--max_frequency |
Maximum frequency of the chirp signal (Hz) |
--duration |
Duration of the chirp signal in seconds |
--device |
Simulation device: cpu, cuda, or cuda:N |
--headless |
Run without rendering |
Example¶
python scripts/pace/data_collection.py \
--task Isaac-Pace-Anymal-D-v0 \
--num_envs 1 \
--min_frequency 0.1 \
--max_frequency 10 \
--duration 20 \
--device cuda
2. Parameter fitting¶
python scripts/pace/fit.py
This script performs system identification using CMA-ES to optimize physical parameters such as armature, damping, friction, bias, and delay.
Common arguments¶
| Argument | Description |
|---|---|
--num_envs |
Number of simulation environments |
--task |
Name of the task / robot |
--device |
Simulation device |
--headless |
Disable rendering for faster execution |
Example¶
python scripts/pace/fit.py \
--task Isaac-Pace-Anymal-D-v0 \
--num_envs 4096 \
--headless
3. Plotting trajectories and scores¶
python scripts/pace/plot_trajectory.py
This utility visualises the optimized trajectory and/or the evolution of the score over iterations. If no folder_name argument is given, the script will use the most recent evolution.
Common arguments¶
| Argument | Description |
|---|---|
--folder_name |
Name of the optimization output folder |
--mean_name |
Name of the parameter file (e.g. mean_020.pt) |
--robot_name |
Name of the robot |
--plot_trajectory |
Plot the optimized joint trajectories |
--plot_score |
Plot the score over iterations |
Example¶
python scripts/pace/plot_trajectory.py \
--folder_name 24_03_12_14-32-10 \
--robot_name anymal_d_sim \
--plot_trajectory
Notes¶
- All scripts share common Isaac Lab launcher arguments such as
--headless,--livestream, and--rendering_mode. - For full argument lists, run any script with:
python script_name.py --help
More advanced workflows and in-depth explanations are covered in the Examples and Guides sections.