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arXiv · 2603.20465

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

Zachary Logan·Andrew Dudash·Daniel Negrón
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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.

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Zachary Logan, Andrew Dudash, Daniel Negrón. 2026-03-20. An Open Source Computer Vision and Machine Learning Framework for Affordable Life Science Robotic Automation. https://arxiv.org/abs/2603.20465

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