  One approach to Imitation Learning is Behavior Cloning, in which a robot
observes a supervisor and infers a control policy. A known problem with this
"off-policy" approach is that the robot's errors compound when drifting away
from the supervisor's demonstrations. On-policy, techniques alleviate this by
iteratively collecting corrective actions for the current robot policy.
However, these techniques can be tedious for human supervisors, add significant
computation burden, and may visit dangerous states during training. We propose
an off-policy approach that injects noise into the supervisor's policy while
demonstrating. This forces the supervisor to demonstrate how to recover from
errors. We propose a new algorithm, DART (Disturbances for Augmenting Robot
Trajectories), that collects demonstrations with injected noise, and optimizes
the noise level to approximate the error of the robot's trained policy during
data collection. We compare DART with DAgger and Behavior Cloning in two
domains: in simulation with an algorithmic supervisor on the MuJoCo tasks
(Walker, Humanoid, Hopper, Half-Cheetah) and in physical experiments with human
supervisors training a Toyota HSR robot to perform grasping in clutter. For
high dimensional tasks like Humanoid, DART can be up to $3x$ faster in
computation time and only decreases the supervisor's cumulative reward by $5\%$
during training, whereas DAgger executes policies that have $80\%$ less
cumulative reward than the supervisor. On the grasping in clutter task, DART
obtains on average a $62\%$ performance increase over Behavior Cloning.
