Leveraging Multi-modal Sensing for Robotic Insertion Tasks in R&D Laboratories

TL;DR

This paper develops a multi-modal sensing approach combining vision, force, and tactile feedback to improve robotic vial insertion accuracy in chemistry labs, enhancing automation and human-robot collaboration.

Contribution

It introduces a multi-modal feedback system for robotic vial insertion, significantly increasing success rates over vision-only methods in laboratory environments.

Findings

Baseline insertion rate of 48.78% with vision only.

Insertion rate improves to 89.55% with multi-modal feedback.

Multi-modal approach enhances automation efficiency in lab tasks.

Abstract

Performing a large volume of experiments in Chemistry labs creates repetitive actions costing researchers time, automating these routines is highly desirable. Previous experiments in robotic chemistry have performed high numbers of experiments autonomously, however, these processes rely on automated machines in all stages from solid or liquid addition to analysis of the final product. In these systems every transition between machine requires the robotic chemist to pick and place glass vials, however, this is currently performed using open loop methods which require all equipment being used by the robot to be in well defined known locations. We seek to begin closing the loop in this vial handling process in a way which also fosters human-robot collaboration in the chemistry lab environment. To do this the robot must be able to detect valid placement positions for the vials it is collecting, and reliably insert them into the detected locations. We create a single modality visual method for estimating placement locations to provide a baseline before introducing two additional methods of feedback (force and tactile feedback). Our visual method uses a combination of classic computer vision methods and a CNN discriminator to detect possible insertion points, then a vial is grasped and positioned above an insertion point and the multi-modal methods guide the final insertion movements using an efficient search pattern. Through our experiments we show the baseline insertion rate of 48.78% improves to 89.55% with the addition of our "force and vision" multi-modal feedback method.