  This work investigates how the intricate task of a continuous pick & place
(P&P) motion may be learned from humans based on demonstrations and
corrections. Due to the complexity of the task, these demonstrations are often
slow and even slightly flawed, particularly at moments when multiple aspects
(i.e., end-effector movement, orientation, and gripper width) have to be
demonstrated at once. Rather than training a person to give better
demonstrations, non-expert users are provided with the ability to interactively
modify the dynamics of their initial demonstration through teleoperated
corrective feedback. This in turn allows them to teach motions outside of their
own physical capabilities. In the end, the goal is to obtain a faster but
reliable execution of the task. The presented framework learns the desired
movement dynamics based on the current Cartesian position with Gaussian
Processes (GPs), resulting in a reactive, time-invariant policy. Using GPs also
allows online interactive corrections and active disturbance rejection through
epistemic uncertainty minimization. The experimental evaluation of the
framework is carried out on a Franka-Emika Panda. Tests were performed to
determine i) the framework's effectiveness in successfully learning how to
quickly pick & place an object, ii) ease of policy correction to environmental
changes (i.e., different object sizes and mass), and iii) the framework's
usability for non-expert users.
