End-to-end automation of behavioural experiments with a collaborative robot
- 1Ecole Polytechnique Federale de Lausanne, School of Life Sciences, Brain Mind Institute, Lausanne, 1015, Switzerland
Abstract
Reliable behavioural measurement is critical to neuroscience discovery, but end-to-end automation of experiments using live animals is challenging. Preparation and transfer of live animals into and out of behavioural assays require skilled, dexterous, gentle and consistent handling. This is difficult to achieve without bespoke automation systems, which resist wide adoption. We present STELA, a robotic cell featuring off-the-shelf collaborative robot (cobot) that autonomously runs the complete workflow of a Drosophila behaviour experiment, from vial to data. Designed to emulate human routine fly laboratory procedures in human workspace, it performs manipulation of fly culture vials, CO2 fly anaesthesia, vision-guided sex classification, single live fly manipulation, and loading and disassembly of a behaviour assay, all in a programmed loop. In 24-h working day, STELA completed 16 experiments (1024 individual visual learning assays in flies) compared to the human who, in an 8-hour working day, completed 6 (384 assays). Retention of flies fit for analysis was similar (93.85% versus 83.33%, respectively), but with 12-fold improvement in handling consistency by the robot. In biologically matched behaviour experiments, robotic and manual handling resulted in the same mean fly behavioural outcomes, yet the robot significantly reduced inter-individual variability in baseline and learning behaviours. STELA demonstrates that it is possible to deploy standard robotic systems to flexibly automate one of the most challenging animal experiments. It provides a low-variance, high-throughput platform for scalable experimentation, and a path to closed-loop, democratized robotic acceleration of experimental discovery.
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BibTeX
@misc{manna2026endtoend,
title = {{End-to-end automation of behavioural experiments with a collaborative robot}},
author = {R. Manna and T. Stampar and I. Tomic and A. M. Jaksic},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.20.758951},
url = {https://www.biorxiv.org/content/10.64898/2026.10.20.758951v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - End-to-end automation of behavioural experiments with a collaborative robot
AU - R. Manna
AU - T. Stampar
AU - I. Tomic
AU - A. M. Jaksic
PY - 2026
DA - 2026/10/20
PB - bioRxiv
DO - 10.64898/2026.10.20.758951
UR - https://www.biorxiv.org/content/10.64898/2026.10.20.758951v1
AB - Reliable behavioural measurement is critical to neuroscience discovery, but end-to-end automation of experiments using live animals is challenging. Preparation and transfer of live animals into and out of behavioural assays require skilled, dexterous, gentle and consistent handling. This is difficult to achieve without bespoke automation systems, which resist wide adoption. We present STELA, a robotic cell featuring off-the-shelf collaborative robot (cobot) that autonomously runs the complete workflow of a Drosophila behaviour experiment, from vial to data. Designed to emulate human routine fly laboratory procedures in human workspace, it performs manipulation of fly culture vials, CO2 fly anaesthesia, vision-guided sex classification, single live fly manipulation, and loading and disassembly of a behaviour assay, all in a programmed loop. In 24-h working day, STELA completed 16 experiments (1024 individual visual learning assays in flies) compared to the human who, in an 8-hour working day, completed 6 (384 assays). Retention of flies fit for analysis was similar (93.85% versus 83.33%, respectively), but with 12-fold improvement in handling consistency by the robot. In biologically matched behaviour experiments, robotic and manual handling resulted in the same mean fly behavioural outcomes, yet the robot significantly reduced inter-individual variability in baseline and learning behaviours. STELA demonstrates that it is possible to deploy standard robotic systems to flexibly automate one of the most challenging animal experiments. It provides a low-variance, high-throughput platform for scalable experimentation, and a path to closed-loop, democratized robotic acceleration of experimental discovery.
ER -