  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.
