  In tasks such as surveying or monitoring remote regions, an autonomous robot
must move while transmitting data over a wireless network with unknown,
position-dependent transmission rates. For such a robot, this paper considers
the problem of transmitting a data buffer in minimum time, while possibly also
navigating towards a goal position. Two approaches are proposed, each
consisting of a machine-learning component that estimates the rate function
from samples; and of an optimal-control component that moves the robot given
the current rate function estimate. Simple obstacle avoidance is performed for
the case without a goal position. In extensive simulations, these methods
achieve competitive performance compared to known-rate and unknown-rate
baselines. A real indoor experiment is provided in which a Parrot AR.Drone 2
successfully learns to transmit the buffer.
