  Versatile movement representations allow robots to learn new tasks and
rapidly adapt them to environmental changes, e.g. introduction of obstacles,
placing additional robots in the workspace, modification of the joint range due
to faults or range of motion constraints due to tool manipulation.
Probabilistic movement primitives (ProMP) model robot movements as a
distribution over trajectories and they are an important tool due to their
analytical tractability and ability to learn and generalise from a small number
of demonstrations. Current approaches solve specific adaptation problems, e.g.
obstacle avoidance, however, a generic probabilistic approach to adaptation has
not yet been developed. In this paper we propose a generic probabilistic
framework for adapting ProMPs. We formulate adaptation as a constrained
optimisation problem where we minimise the Kullback-Leibler divergence between
the adapted distribution and the distribution of the original primitive and we
constrain the probability mass associated with undesired trajectories to be
low. We derive several types of constraints that can be added depending on the
task, such us joint limiting, various types of obstacle avoidance, via-points,
and mutual avoidance, under a common framework. We demonstrate our approach on
several adaptation problems on simulated planar robot arms and 7-DOF
Franka-Emika robots in single and dual robot arm settings.
