  Corrective interventions while a robot is learning to automate a task provide
an intuitive method for a human supervisor to assist the robot and convey
information about desired behavior. However, these interventions can impose
significant burden on a human supervisor, as each intervention interrupts other
work the human is doing, incurs latency with each context switch between
supervisor and autonomous control, and requires time to perform. We present
LazyDAgger, which extends the interactive imitation learning (IL) algorithm
SafeDAgger to reduce context switches between supervisor and autonomous
control. We find that LazyDAgger improves the performance and robustness of the
learned policy during both learning and execution while limiting burden on the
supervisor. Simulation experiments suggest that LazyDAgger can reduce context
switches by an average of 60% over SafeDAgger on 3 continuous control tasks
while maintaining state-of-the-art policy performance. In physical fabric
manipulation experiments with an ABB YuMi robot, LazyDAgger reduces context
switches by 60% while achieving a 60% higher success rate than SafeDAgger at
execution time.
