  The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed
that robots can adapt to damage in a matter of a few trials. The success of
this algorithm relies on two components: prior knowledge acquired through
simulation with an intact robot, and Bayesian optimization (BO) that operates
on-line, on the damaged robot. While IT&E leads to fast damage recovery, it
does not incorporate any safety constraints that prevent the robot from
attempting harmful behaviors. In this work, we address this limitation by
replacing the BO component with a constrained BO procedure. We evaluate our
approach on a simulated damaged humanoid robot that needs to crawl as fast as
possible, while performing as few unsafe trials as possible. We compare our new
"safety-aware IT&E" algorithm to IT&E and a multi-objective version of IT&E in
which the safety constraints are dealt as separate objectives. Our results show
that our algorithm outperforms the other approaches, both in crawling speed
within the safe regions and number of unsafe trials.
