  Ensuring safety is of paramount importance in physical human-robot
interaction applications. This requires both adherence to safety constraints
defined on the system state, as well as guaranteeing compliant behavior of the
robot. If the underlying dynamical system is known exactly, the former can be
addressed with the help of control barrier functions. The incorporation of
elastic actuators in the robot's mechanical design can address the latter
requirement. However, this elasticity can increase the complexity of the
resulting system, leading to unmodeled dynamics, such that control barrier
functions cannot directly ensure safety. In this paper, we mitigate this issue
by learning the unknown dynamics using Gaussian process regression. By
employing the model in a feedback linearizing control law, the safety
conditions resulting from control barrier functions can be robustified to take
into account model errors, while remaining feasible. In order to enforce them
on-line, we formulate the derived safety conditions in the form of a
second-order cone program. We demonstrate our proposed approach with
simulations on a two-degree-of-freedom planar robot with elastic joints.
