  Robotic assistance for experimental manipulation in the life sciences is
expected to enable favorable outcomes, regardless of the skill of the
scientist. Experimental specimens in the life sciences are subject to
individual variability hence require intricate algorithms for successful
autonomous robotic control. As a use case, we are studying the creation of
cranial windows in mice. This operation requires the removal of an
8-mm-circular patch of the skull, which is approximately 300 um thick, but the
shape and thickness of the mouse skull significantly varies depending on the
strain of mouse, sex, and age. In this work, we propose an autonomous robotic
drilling method with no offline planning, consisting of a trajectory planning
block with execution-time feedback with completion level recognition based on
image and force information. The force information allows for completion-level
resolution to increase 10 fold. We evaluate the proposed method in two ways.
First, in an eggshell drilling task and achieved a success rate of 95% and
average drilling time of 7.1 min out of 20 trials. Second, in postmortem mice
and with a success rate of 70% and average drilling time of 9.3 min out of 20
trials.
