# chengxuxin/extreme-parkour

[ICRA 2024]: Train your parkour robot in less than 20 hours.

Repository: https://github.com/chengxuxin/extreme-parkour
Canonical: https://ross.abutalabs.com/products/extreme-parkour
Homepage: https://extreme-parkour.github.io
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-11-28T13:18:03+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 76
- inputs: {"age_days": 1072, "days_push": 1009, "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 1161, forks 179 (observed 2026-08-28T04:03:49.353988+00:00)

## What it is
Official code for 'Extreme Parkour with Legged Robots' (ICRA 2024), a reinforcement learning framework for training quadruped robots to perform parkour using Isaac Gym simulation. It trains a single neural network policy end-to-end from depth camera images to precise locomotion control, deployable to low-cost hardware in under 20 hours of training.

## Use cases
- train a quadruped robot to climb and jump obstacles
- simulate legged robot locomotion with reinforcement learning
- distill a privileged policy into a camera-based vision policy
- deploy RL locomotion policies to real robots
- reproduce parkour robot research results
- train robot policies on a single GPU

## When to choose
- you want to train vision-based legged locomotion policies in simulation
- you have an NVIDIA GPU and Isaac Gym and want a proven sim-to-real quadruped pipeline
- you are reproducing or extending the Extreme Parkour paper

## When to avoid
- you need a maintained production robotics framework with support and documentation
- you don't have access to NVIDIA Isaac Gym or a CUDA GPU
- you work with non-quadruped robots or manipulation tasks

## Facets
- artifact type: library
- maturity: active
- function: reinforcement-learning, simulation, machine-learning, robotics
- domain: robotics, reinforcement-learning, simulation, machine-learning
- platform: python
- tags: legged-robots, isaac-gym, sim-to-real, quadruped, locomotion, research-code, parkour, linux, gpu

## Member repositories
- chengxuxin/extreme-parkour (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:49.353988+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 2026-08-30T06:31:41.886483+00:00, confidence not recorded.
  - readme: https://github.com/chengxuxin/extreme-parkour (fetched 2026-08-28T04:03:49.353988+00:00, sha 36e9b7e8193f)
  - homepage: https://extreme-parkour.github.io (fetched 2026-08-29T12:36:13.499279+00:00, sha 568653d12b3b)
- Data as of 2026-08-30T08:39:29.467469+00:00.