  End-to-end visuomotor control is emerging as a compelling solution for robot
manipulation tasks. However, imitation learning-based visuomotor control
approaches tend to suffer from a common limitation, lacking the ability to
recover from an out-of-distribution state caused by compounding errors. In this
paper, instead of using tactile feedback or explicitly detecting the failure
through vision, we investigate using the uncertainty of a policy neural
network. We propose a novel uncertainty-based approach to detect and recover
from failure cases. Our hypothesis is that policy uncertainties can implicitly
indicate the potential failures in the visuomotor control task and that robot
states with minimum uncertainty are more likely to lead to task success. To
recover from high uncertainty cases, the robot monitors its uncertainty along a
trajectory and explores possible actions in the state-action space to bring
itself to a more certain state. Our experiments verify this hypothesis and show
a significant improvement on task success rate: 12% in pushing, 15% in
pick-and-reach and 22% in pick-and-place.
