  Agile robotics presents a difficult challenge with robots moving at high
speeds requiring precise and low-latency sensing and control. Creating agile
motion that accomplishes the task at hand while being safe to execute is a key
requirement for agile robots to gain human trust. This requires designing new
approaches that are flexible and maintain knowledge over world constraints. In
this paper, we consider the problem of building a flexible and adaptive
controller for a challenging agile mobile manipulation task of hitting ground
strokes on a wheelchair tennis robot. We propose and evaluate an extension to
work done on learning striking behaviors using a probabilistic movement
primitive (ProMP) framework by (1) demonstrating the safe execution of learned
primitives on an agile mobile manipulator setup, and (2) proposing an online
primitive refinement procedure that utilizes evaluative feedback from humans on
the executed trajectories.
