# OSCAR: A Modular Open-Source Robotic Platform for Biological Laboratories

## TL;DR

OSCAR is a low-cost, open-source robotic platform designed to automate common lab tasks, making automation more accessible and flexible for biological research.

## Contribution

The novel contribution is the development of a modular, open-source robotic system that integrates standard lab equipment for diverse biological workflows.

## Key findings

- OSCAR successfully performed a complete plasmid assembly workflow, including PCR, assembly, transformation, and validation.
- The platform uses a dual-function robotic arm with pipetting and vision-enabled gripping capabilities.
- OSCAR is compatible with standard lab consumables and equipment, reducing reliance on proprietary systems.

## Abstract

Biological research often involves complex, repetitive,
and high-throughput
manipulations that are well-suited to automation. However, current
robotic systems generally excel only at narrowly defined tasks or
standardized workflows and remain expensive, inflexible, and dependent
on proprietary modules or reagents. To address these limitations,
we developed the Open-Source Collaborative Automation & Robotics
(OSCAR) platform, a flexible and low-cost system designed to perform
common laboratory manipulations using standard, human-operated equipment.
OSCAR incorporates open-source software and modular hardware to maximize
accessibility and affordability. The platform features a robotic arm
equipped with a dual-function end-effector: a pipetting module for
precise liquid handling and a vision-enabled gripper for manipulating
laboratory tools. To demonstrate the platform’s versatility,
we implemented a representative plasmid assembly workflow, from PCR
amplification and enzymatic assembly to transformation, plating, colony
picking, PCR screening, and validation by agarose gel electrophoresis.
By making this system open-source and compatible with widely used
consumables and equipment, we aim to democratize access to automation
and broaden its adoption across diverse research environments.