  In this paper, we consider the problem of adapting a dynamically walking
bipedal robot to follow a leading co-worker while engaging in tasks that
require physical interaction. Our approach relies on switching among a family
of Dynamic Movement Primitives (DMPs) as governed by a supervisor. We train the
supervisor to orchestrate the switching among the DMPs in order to adapt to the
leader's intentions, which are only implicitly available in the form of
interaction forces. The primary contribution of our approach is its ability to
furnish certificates of generalization to novel leader intentions for the
trained supervisor. This is achieved by leveraging the Probably Approximately
Correct (PAC)-Bayes bounds from generalization theory. We demonstrate the
efficacy of our approach by training a neural-network supervisor to adapt the
gait of a dynamically walking biped to a leading collaborator whose intended
trajectory is not known explicitly.
