  Over the past few years, there have been numerous works towards advancing the
generalization capability of robots, among which learning from demonstrations
(LfD) has drawn much attention by virtue of its user-friendly and
data-efficient nature. While many LfD solutions have been reported, a key
question has not been properly addressed: how can we evaluate the
generalization performance of LfD? For instance, when a robot draws a letter
that needs to pass through new desired points, how does it ensure the new
trajectory maintains a similar shape to the demonstration? This question
becomes more relevant when a new task is significantly far from the
demonstrated region. To tackle this issue, a user often resorts to manual
tuning of the hyperparameters of an LfD approach until a satisfactory
trajectory is attained. In this paper, we aim to provide closed-loop evaluative
feedback for LfD and optimize LfD in an automatic fashion. Specifically, we
consider dynamical movement primitives (DMP) and kernelized movement primitives
(KMP) as examples and develop a generic optimization framework capable of
measuring the generalization performance of DMP and KMP and auto-optimizing
their hyperparameters without any human inputs. Evaluations including a
peg-in-hole task and a pushing task on a real robot evidence the applicability
of our framework.
