# HybridRobotics/whole_body_tracking

Repository: https://github.com/HybridRobotics/whole_body_tracking
Canonical: https://ross.abutalabs.com/products/whole_body_tracking
Language: Python
License: MIT
License Family: permissive
Last push: 2026-07-24T09:41:55+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 62, longevity 27
- inputs: {"age_days": 387, "days_push": 40, "days_rel": 40, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2344, forks 321 (observed 2026-08-28T04:06:39.617350+00:00)

## What it is
BeyondMimic's official motion tracking training code, a humanoid control framework built on Isaac Lab that trains sim-to-real-ready whole-body motion tracking policies. It supports training on LAFAN1 dataset motions without parameter tuning and uses guided diffusion-based controllers for steerable test-time control.

## Use cases
- train humanoid motion tracking policies in simulation
- sim-to-real deployment of dynamic whole-body motions on humanoid robots
- reproduce BeyondMimic motion tracking results
- train tracking policies for LAFAN1 motions without tuning
- research diffusion-based controllers for humanoid control
- learn reinforcement learning for legged robot control with Isaac Lab

## When to choose
- you need state-of-the-art humanoid motion tracking with sim-to-real transfer
- you work with Isaac Lab/Isaac Sim and Unitree humanoid robots
- you want to train LAFAN1 motions out of the box

## When to avoid
- you need actual robot deployment code - use the companion motion_tracking_controller repo
- you don't have access to NVIDIA Isaac Sim/Isaac Lab or a GPU
- you need MuJoCo-native tooling - consider the mjlab alternative implementation

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, reinforcement-learning, simulation, robotics
- domain: robotics, machine-learning, simulation, deep-learning
- platform: python
- tags: humanoid-robotics, motion-tracking, sim-to-real, reinforcement-learning, isaac-lab, diffusion-policy, unitree, linux, gpu

## Member repositories
- HybridRobotics/whole_body_tracking (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:39.617350+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:37:03.940750+00:00, confidence not recorded.
  - readme: https://github.com/HybridRobotics/whole_body_tracking (fetched 2026-08-28T04:06:39.617350+00:00, sha 69eb0b37c44e)
- Data as of 2026-08-30T08:39:29.467469+00:00.