  Motivated by recent advances in Deep Learning for robot control, this paper
considers two learning algorithms in terms of how they acquire demonstrations.
"Human-Centric" (HC) sampling is the standard supervised learning algorithm,
where a human supervisor demonstrates the task by teleoperating the robot to
provide trajectories consisting of state-control pairs. "Robot-Centric" (RC)
sampling is an increasingly popular alternative used in algorithms such as
DAgger, where a human supervisor observes the robot executing a learned policy
and provides corrective control labels for each state visited. RC sampling can
be challenging for human supervisors and prone to mislabeling. RC sampling can
also induce error in policy performance because it repeatedly visits areas of
the state space that are harder to learn. Although policies learned with RC
sampling can be superior to HC sampling for standard learning models such as
linear SVMs, policies learned with HC sampling may be comparable with
highly-expressive learning models such as deep learning and hyper-parametric
decision trees, which have little model error. We compare HC and RC using a
grid world and a physical robot singulation task, where in the latter the input
is a binary image of a connected set of objects on a planar worksurface and the
policy generates a motion of the gripper to separate one object from the rest.
We observe in simulation that for linear SVMs, policies learned with RC
outperformed those learned with HC but that with deep models this advantage
disappears. We also find that with RC, the corrective control labels provided
by humans can be highly inconsistent. We prove there exists a class of examples
where in the limit, HC is guaranteed to converge to an optimal policy while RC
may fail to converge.
