# AlexanderKoch-Koch/low_cost_robot

Repository: https://github.com/AlexanderKoch-Koch/low_cost_robot
Canonical: https://ross.abutalabs.com/products/low_cost_robot
Language: Python
License: MIT
License Family: permissive
Last push: 2024-09-21T17:21:21+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 63
- inputs: {"age_days": 894, "days_push": 711, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3408, forks 301 (observed 2026-08-28T04:08:03.193368+00:00)

## What it is
An open-source project with files and Python control code for building a ~$250 low-cost robot arm using Dynamixel XL430 and XL330 servo motors, plus a ~$180 leader arm for teleoperation. It is aimed at hobbyists and researchers doing robot learning experiments.

## Use cases
- build a cheap robot arm for robot learning experiments
- teleoperate a follower arm with a leader arm
- control Dynamixel servo motors from Python
- collect demonstration data for imitation learning
- build a low-cost teleoperation setup like GELLO

## When to choose
- you want an affordable robot arm for research or hobby projects
- you need a leader-follower teleoperation setup for robot learning
- you want open hardware designs and Python control code you can modify

## When to avoid
- you need an industrial-grade or high-payload robot arm
- you want a ready-made commercial product with support
- you are not comfortable assembling hardware and sourcing parts

## Facets
- artifact type: application
- maturity: active
- function: robotics, sdk, developer-tools
- domain: robotics, hardware, machine-learning
- platform: python, cross-platform
- tags: robot-arm, dynamixel, teleoperation, robot-learning, leader-follower, diy-robotics, linux

## Member repositories
- AlexanderKoch-Koch/low_cost_robot (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:03.193368+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 2026-08-29T18:38:32.817411+00:00, confidence not recorded.
  - readme: https://github.com/AlexanderKoch-Koch/low_cost_robot (fetched 2026-08-28T04:08:03.193368+00:00, sha a1e84ef7ca04)
- Data as of 2026-08-30T08:39:29.467469+00:00.