# An Open Source Computer Vision and Machine Learning Framework for Affordable Life Science Robotic Automation

Source: [An Open Source Computer Vision and Machine Learning Framework for Affordable Life Science Robotic Automation](<https://www.sota2.com/research/paper/69c21a8f0636e5bf4afc48ad>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-25T05:00:04.214Z

Authors: Zachary Logan, Andrew Dudash, Daniel Negr\\'on

Published: 2026-03-20T19:51:28.000Z

## Abstract

We present an open-source robotic framework that integrates computer vision and machine learning based inverse kinematics to enable low-cost laboratory automation tasks such as colony picking and liquid handling. The system uses a custom trained U-net model for semantic segmentation of microbial cultures, combined with Mixture Density Network for predicating joint angles of a simple 5-DOF robot arm. We evaluated the framework using a modified robot arm, upgraded with a custom liquid handling end-effector. Experimental results demonstrate the framework's feasibility for precise, repeatable operations, with mean positional error below 1 mm and joint angle prediction errors below 4 degrees and colony detection capabilities with IoU score of 0.537 and Dice coefficient of 0.596.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.20465>)

[PDF](<https://arxiv.org/pdf/2603.20465.pdf>)