# booster_train

**Repository Path**: v1ux/booster_train

## Basic Information

- **Project Name**: booster_train
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-05-30
- **Last Updated**: 2026-05-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Booster RL Tasks

## Overview

This repository provides a set of reinforcement learning tasks for Booster robots using [Isaac Lab](https://isaac-sim.github.io/IsaacLab/main/index.html).
Currently it includes the fabulous [BeyondMimic motion tracking](https://github.com/HybridRobotics/whole_body_tracking) framework adapted to Booster K1 robots.
This repository follows the standard Isaac Lab project structure, and is tested with IsaacLab 2.2 and Isaac Sim 5.0.

## Installation

- Install Isaac Lab by following the [installation guide](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/index.html).
  We recommend using the conda installation as it simplifies calling Python scripts from the terminal.

- Clone or copy this project/repository separately from the Isaac Lab installation (i.e. outside the `IsaacLab` directory):
    ```bash
    git clone https://github.com/BoosterRobotics/booster_train.git
    ```

- Download and install booster_assets:
   - Clone the [booster_assets](https://github.com/BoosterRobotics/booster_assets) which contains Booster robot models and motion data.
   - Install booster_assets python helper following the instructions in the repository.

- Using a python interpreter that has Isaac Lab installed, install the library in editable mode using:

    ```bash
    # use 'PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda
    python -m pip install -e source/booster_train
    ```

- Prepare BeyondMimic motion data:
    ```bash
    # use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda
    python scripts/csv_to_npz.py --headless --input_file=<PATH_TO_BOOSTER_ASSETS>/motions/K1/<MOTION>.csv --input_fps=<FPS> --output_name=<PATH_TO_BOOSTER_ASSETS>/motions/K1/<MOTION>.npz
    python scripts\csv_to_npz.py  --input_file "C:\Users\do\projs\booster_assets\motions\K1\k1_fight_001_30fps.csv" --input_fps 30 --output_name "C:\Users\do\projs\booster_assets\motions\K1\k1_fight_001_30fps.npz"
    ```

## Usage

- Listing the available tasks:

    ```bash
    # use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda
    python scripts/list_envs.py
    ```

- Running a task:

    ```bash
    # use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda
    python scripts/rsl_rl/train.py --task=<TASK_NAME> --headless --device cuda:N\
    python scripts/rsl_rl/train.py --task=Booster-K1-MJ_Dance_002-v0 --device cuda:N
    ```

- Play a trained policy and export it for deployment:

    ```bash
    # use 'FULL_PATH_TO_isaaclab.sh|bat -p' instead of 'python' if Isaac Lab is not installed in Python venv or conda
    python scripts/rsl_rl/play.py --task=<TASK_NAME> --checkpoint=<CHECKPOINT_PATH>
    ```

    This script also exports the trained policy to a TorchScript/ONNX file for deployment on real robots in `logs/rsl_rl/<EXPERIMENT>/<RUN>/exported/`.

## Deploy

After a model has been trained and exported, you can deploy the trained policy in MuJoCo or on real Booster robots using the [booster_deploy](https://github.com/BoosterRobotics/booster_deploy) repository. For more details, please refer to the instructions in the [booster_deploy](https://github.com/BoosterRobotics/booster_deploy) repository.


## Acknowledgements

- [whole_body_tracking](https://github.com/HybridRobotics/whole_body_tracking): the motion tracking training in BeyondMimic, which is a versatile humanoid control framework that provides highly dynamic motion tracking.