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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.20465 |
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| _version_ | 1866915878181797888 |
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| author | Logan, Zachary Dudash, Andrew Negrón, Daniel |
| author_facet | Logan, Zachary Dudash, Andrew Negrón, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20465 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Open Source Computer Vision and Machine Learning Framework for Affordable Life Science Robotic Automation Logan, Zachary Dudash, Andrew Negrón, Daniel Robotics 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. |
| title | An Open Source Computer Vision and Machine Learning Framework for Affordable Life Science Robotic Automation |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.20465 |