# whole_body_tracking

**Repository Path**: cvsuser/whole_body_tracking

## Basic Information

- **Project Name**: whole_body_tracking
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-27
- **Last Updated**: 2025-11-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# BeyondMimic Motion Tracking Code

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[[Website]](https://beyondmimic.github.io/)
[[Arxiv]](https://arxiv.org/abs/2508.08241)
[[Video]](https://youtu.be/RS_MtKVIAzY)

## Overview

BeyondMimic is a versatile humanoid control framework that provides highly dynamic motion tracking with the
state-of-the-art motion quality on real-world deployment and steerable test-time control with guided diffusion-based
controllers.

This repo covers the motion tracking training in BeyondMimic. **You should be able to
train any sim-to-real-ready motion in the LAFAN1 dataset, without tuning any parameters**.

For sim-to-sim and sim-to-real deployment, please refer to
the [motion_tracking_controller](https://github.com/HybridRobotics/motion_tracking_controller).

### Alternative Implementations

- There is an alternative reproduction of BeyondMimic in [mjlab](https://github.com/mujocolab/mjlab), a new Isaac Lab-style manager API powered by MuJoCo-Warp for RL and robotics research. See the implementation [here](https://github.com/mujocolab/mjlab/blob/main/src/mjlab/tasks/tracking/tracking_env_cfg.py).

## Installation

- Install Isaac Lab v2.1.0 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 this repository separately from the Isaac Lab installation (i.e., outside the `IsaacLab` directory):

```bash
# Option 1: SSH
git clone git@github.com:HybridRobotics/whole_body_tracking.git

# Option 2: HTTPS
git clone https://github.com/HybridRobotics/whole_body_tracking.git
```

- Pull the robot description files from GCS

```bash
# Enter the repository
cd whole_body_tracking
# Rename all occurrences of whole_body_tracking (in files/directories) to your_fancy_extension_name
curl -L -o unitree_description.tar.gz https://storage.googleapis.com/qiayuanl_robot_descriptions/unitree_description.tar.gz && \
tar -xzf unitree_description.tar.gz -C source/whole_body_tracking/whole_body_tracking/assets/ && \
rm unitree_description.tar.gz
```

- Using a Python interpreter that has Isaac Lab installed, install the library

```bash
python -m pip install -e source/whole_body_tracking
```

## Motion Tracking

### Motion Preprocessing & Registry Setup

In order to manage the large set of motions we used in this work, we leverage the WandB registry to store and load
reference motions automatically.
Note: The reference motion should be retargeted and use generalized coordinates only.

- Gather the reference motion datasets (please follow the original licenses), we use the same convention as .csv of
  Unitree's dataset

    - Unitree-retargeted LAFAN1 Dataset is available
      on [HuggingFace](https://huggingface.co/datasets/lvhaidong/LAFAN1_Retargeting_Dataset)
    - Sidekicks are from [KungfuBot](https://kungfu-bot.github.io/)
    - Christiano Ronaldo celebration is from [ASAP](https://github.com/LeCAR-Lab/ASAP).
    - Balance motions are from [HuB](https://hub-robot.github.io/)


- Log in to your WandB account; access Registry under Core on the left. Create a new registry collection with the name "
  Motions" and artifact type "All Types".


- Convert retargeted motions to include the maximum coordinates information (body pose, body velocity, and body
  acceleration) via forward kinematics,

```bash
python scripts/csv_to_npz.py --input_file {motion_name}.csv --input_fps 30 --output_name {motion_name} --headless
```

This will automatically upload the processed motion file to the WandB registry with output name {motion_name}.

- Test if the WandB registry works properly by replaying the motion in Isaac Sim:

```bash
python scripts/replay_npz.py --registry_name={your-organization}-org/wandb-registry-motions/{motion_name}
```

- Debugging
    - Make sure to export WANDB_ENTITY to your organization name, not your personal username.
    - If /tmp folder is not accessible, modify csv_to_npz.py L319 & L326 to a temporary folder of your choice.

### Policy Training

- Train policy by the following command:

```bash
python scripts/rsl_rl/train.py --task=Tracking-Flat-G1-v0 \
--registry_name {your-organization}-org/wandb-registry-motions/{motion_name} \
--headless --logger wandb --log_project_name {project_name} --run_name {run_name}
```

### Policy Evaluation

- Play the trained policy by the following command:

```bash
python scripts/rsl_rl/play.py --task=Tracking-Flat-G1-v0 --num_envs=2 --wandb_path={wandb-run-path}
```

The WandB run path can be located in the run overview. It follows the format {your_organization}/{project_name}/ along
with a unique 8-character identifier. Note that run_name is different from run_path.

## Code Structure

Below is an overview of the code structure for this repository:

- **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/mdp`**
  This directory contains the atomic functions to define the MDP for BeyondMimic. Below is a breakdown of the functions:

    - **`commands.py`**
      Command library to compute relevant variables from the reference motion, current robot state, and error
      computations. This includes pose and velocity error calculation, initial state randomization, and adaptive
      sampling.

    - **`rewards.py`**
      Implements the DeepMimic reward functions and smoothing terms.

    - **`events.py`**
      Implements domain randomization terms.

    - **`observations.py`**
      Implements observation terms for motion tracking and data collection.

    - **`terminations.py`**
      Implements early terminations and timeouts.

- **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/tracking_env_cfg.py`**
  Contains the environment (MDP) hyperparameters configuration for the tracking task.

- **`source/whole_body_tracking/whole_body_tracking/tasks/tracking/config/g1/agents/rsl_rl_ppo_cfg.py`**
  Contains the PPO hyperparameters for the tracking task.

- **`source/whole_body_tracking/whole_body_tracking/robots`**
  Contains robot-specific settings, including armature parameters, joint stiffness/damping calculation, and action scale
  calculation.

- **`scripts`**
  Includes utility scripts for preprocessing motion data, training policies, and evaluating trained policies.

This structure is designed to ensure modularity and ease of navigation for developers expanding the project.