  A common approach to learn robotic skills is to imitate a demonstrated
policy. Due to the compounding of small errors and perturbations, this approach
may let the robot leave the states in which the demonstrations were provided.
This requires the consideration of additional strategies to guarantee that the
robot will behave appropriately when facing unknown states. We propose to use a
Bayesian method to quantify the action uncertainty at each state. The proposed
Bayesian method is simple to set up, computationally efficient, and can adapt
to a wide range of problems. Our approach exploits the estimated uncertainty to
fuse the imitation policy with additional policies. It is validated on a Panda
robot with the imitation of three manipulation tasks in the continuous domain
using different control input/state pairs.
