  Predictions and predictive knowledge have seen recent success in improving
not only robot control but also other applications ranging from industrial
process control to rehabilitation. A property that makes these predictive
approaches well suited for robotics is that they can be learned online and
incrementally through interaction with the environment. However, a remaining
challenge for many prediction-learning approaches is an appropriate choice of
prediction-learning parameters, especially parameters that control the
magnitude of a learning machine's updates to its predictions (the learning rate
or step size). To begin to address this challenge, we examine the use of online
step-size adaptation using a sensor-rich robotic arm. Our method of choice,
Temporal-Difference Incremental Delta-Bar-Delta (TIDBD), learns and adapts step
sizes on a feature level; importantly, TIDBD allows step-size tuning and
representation learning to occur at the same time. We show that TIDBD is a
practical alternative for classic Temporal-Difference (TD) learning via an
extensive parameter search. Both approaches perform comparably in terms of
predicting future aspects of a robotic data stream. Furthermore, the use of a
step-size adaptation method like TIDBD appears to allow a system to
automatically detect and characterize common sensor failures in a robotic
application. Together, these results promise to improve the ability of robotic
devices to learn from interactions with their environments in a robust way,
providing key capabilities for autonomous agents and robots.
