# LeCAR-Lab/human2humanoid

[IROS 2024] Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation.                    [CoRL 2024] OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning

Repository: https://github.com/LeCAR-Lab/human2humanoid
Canonical: https://ross.abutalabs.com/products/human2humanoid
Homepage: https://omni.human2humanoid.com/
Language: Python
License Family: other
Last push: 2025-02-21T06:58:48+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 7, release rhythm 35, longevity 50
- inputs: {"age_days": 703, "days_push": 558, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1060, forks 100 (observed 2026-08-28T04:03:25.650991+00:00)

## What it is
Official implementation of H2O and OmniH2O, reinforcement-learning-based systems for real-time whole-body teleoperation of full-sized humanoid robots using human motion (VR, RGB camera, or verbal input). It includes training pipelines built on Isaac Gym, Legged Gym, and RSL RL, plus the OmniH2O-6 whole-body control dataset.

## Use cases
- teleoperate a humanoid robot with a VR headset or RGB camera
- train RL policies for humanoid whole-body control in simulation
- learn autonomous humanoid skills from teleoperated demonstrations
- retarget human motion capture data to humanoid robots
- run sim-to-real deployment of humanoid locomotion policies

## When to choose
- you need state-of-the-art human-to-humanoid whole-body teleoperation with a real-time control interface
- you want a research codebase for RL-based humanoid control on Isaac Gym
- you need a humanoid whole-body skill dataset for imitation learning

## When to avoid
- you need a permissively licensed library for commercial products - the code is CC BY-NC 4.0
- you don't have access to NVIDIA Isaac Gym or a CUDA GPU
- you need a production-ready robotics stack rather than research code

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, reinforcement-learning, simulation, robotics
- domain: robotics, deep-learning, autonomous-vehicles
- platform: python
- tags: humanoid-robotics, teleoperation, sim-to-real, isaac-gym, motion-capture, research-code, non-commercial-license, gpu, linux

## Member repositories
- LeCAR-Lab/human2humanoid (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:25.650991+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 2026-08-30T06:56:54.251785+00:00, confidence not recorded.
  - readme: https://github.com/LeCAR-Lab/human2humanoid (fetched 2026-08-28T04:03:25.650991+00:00, sha e3e68c3bfb9b)
  - homepage: https://omni.human2humanoid.com/ (fetched 2026-08-29T12:58:55.727622+00:00, sha ee49d8cee402)
- Data as of 2026-08-30T08:39:29.467469+00:00.