  We present a data-driven optimization approach for robotic controlled
deposition with a degradable tool. Existing methods make the assumption that
the tool tip is not changing or is replaced frequently. Errors can accumulate
over time as the tool wears away and this leads to poor performance in the case
where the tool degradation is unaccounted for during deposition. In the
proposed approach, we utilize visual and force feedback to update the unknown
model parameters of our tool-tip. Subsequently, we solve a constrained finite
time optimal control problem for tracking a reference deposition profile, where
our robot plans with the learned tool degradation dynamics. We focus on a
robotic drawing problem as an illustrative example. Using real-world
experiments, we show that the error in target vs actual deposition decreases
when learned degradation models are used in the control design.
