Multimodal Behaviour Trees for Robotic Laboratory Task Automation

TL;DR

This paper introduces a multimodal behaviour tree framework for robotic laboratory tasks, enhancing reliability and safety verification in automation, demonstrated through high success rates in vial capping and rack insertion.

Contribution

It presents a novel multimodal perception-based behaviour tree methodology for automating and verifying laboratory robotic tasks, improving safety and reliability.

Findings

88% success rate in vial capping

92% success rate in rack insertion

Strong error detection capabilities

Abstract

Laboratory robotics offer the capability to conduct experiments with a high degree of precision and reproducibility, with the potential to transform scientific research. Trivial and repeatable tasks; e.g., sample transportation for analysis and vial capping are well-suited for robots; if done successfully and reliably, chemists could contribute their efforts towards more critical research activities. Currently, robots can perform these tasks faster than chemists, but how reliable are they? Improper capping could result in human exposure to toxic chemicals which could be fatal. To ensure that robots perform these tasks as accurately as humans, sensory feedback is required to assess the progress of task execution. To address this, we propose a novel methodology based on behaviour trees with multimodal perception. Along with automating robotic tasks, this methodology also verifies the successful execution of the task, a fundamental requirement in safety-critical environments. The experimental evaluation was conducted on two lab tasks: sample vial capping and laboratory rack insertion. The results show high success rate, i.e., 88% for capping and 92% for insertion, along with strong error detection capabilities. This ultimately proves the robustness and reliability of our approach and that using multimodal behaviour trees should pave the way towards the next generation of robotic chemists.