# Improbable-AI/walk-these-ways

Sim-to-real RL training and deployment tools for the Unitree Go1 robot.

Repository: https://github.com/Improbable-AI/walk-these-ways
Canonical: https://ross.abutalabs.com/products/walk-these-ways
Homepage: https://gmargo11.github.io/walk-these-ways/
Language: Python
License: NOASSERTION
License Family: other
Topics: go1, sim-to-real, unitree, reinforcement-learning, robotics
Last push: 2024-06-16T22:12:27+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 99
- inputs: {"age_days": 1387, "days_push": 808, "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 1438, forks 222 (observed 2026-08-28T04:04:43.967963+00:00)

## What it is
A sim-to-real reinforcement learning starter kit for the Unitree Go1 quadruped robot, implementing the Walk These Ways (MoB) locomotion controller. It trains PPO policies in NVIDIA Isaac Gym with domain randomization and deploys them on the real robot via the unitree_legged_sdk.

## Use cases
- train RL locomotion policies for the Unitree Go1
- deploy a learned walking controller on a quadruped robot
- simulate legged robot training with Isaac Gym
- run a pretrained multiplicity-of-behavior locomotion policy
- transfer simulated robot policies to the real world
- tune gait, footswing, and posture for a quadruped

## When to choose
- you have a Unitree Go1 and want a proven sim-to-real locomotion pipeline
- you need a research baseline for legged RL with domain randomization
- you want a single policy that switches between diverse gaits at runtime

## When to avoid
- you use a robot other than the Unitree Go1
- you need a maintained, actively updated framework
- you lack an NVIDIA GPU for Isaac Gym training

## Facets
- artifact type: library
- maturity: maintenance
- function: reinforcement-learning, simulation, robotics, machine-learning, llm-training
- domain: robotics, reinforcement-learning, simulation, machine-learning
- platform: python
- tags: sim-to-real, quadruped, unitree-go1, isaac-gym, legged-locomotion, ppo, domain-randomization, robot-deployment, linux, gpu

## Member repositories
- Improbable-AI/walk-these-ways (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:43.967963+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 2026-08-30T04:36:38.608072+00:00, confidence not recorded.
  - readme: https://github.com/Improbable-AI/walk-these-ways (fetched 2026-08-28T04:04:43.967963+00:00, sha 11a6f0c15d02)
  - homepage: https://gmargo11.github.io/walk-these-ways/ (fetched 2026-08-29T11:47:12.380553+00:00, sha 12fc5af26248)
- Data as of 2026-08-30T08:39:29.467469+00:00.