Computer Science Department
School of Computer Science, Carnegie Mellon University
Learning To Prevent Failure State
for a Dynamically Balancing Robot
Jeremy Searock, Brett Browning, Manuela Veloso
Keywords: Failure prevention, state identification
To achieve robust autonomy, robots must avoid getting stuck in
states from which they cannot recover without external aid.
While this is the role of the robot's control algorithms,
these are often imperfect. We examine how to detect failures
by observing the robot's internal sensors over time. For such
cases, triggering a response when detecting the onset of a
failure can increase the operational range of the robot.
Concretely, we explore the use of supervised learning techniques
to create a classifier that can detect a potential failure
and trigger a response for a dynamically balancing robot.
We present a fully implemented system, where the results
clearly demonstrate an improved safety margin for the robot.