C. Kwok and D. Fox
Reinforcement Learning for Sensing Strategies.
Proceedings of IROS, 2004.
Abstract
Since sensors have limited range and coverage, mobile robots often
have to make decisions on where to point their sensors. A good
sensing strategy allows a robot to collect information that is
useful for its tasks. Most existing solutions to this {\em active
sensing} problem choose the direction that maximally reduces the
uncertainty in a single state variable. In more complex problem
domains, however, uncertainties exist in multiple state variables,
and they affect the performance of the robot in different ways. The
robot thus needs to have more sophisticated sensing strategies in
order to decide which uncertainties to reduce, and to make the
correct trade-offs. In this work, we apply a least squares
reinforcement learning method to solve this problem. We implemented
and tested the learning approach in the RoboCup domain, where the
robot attempts to reach a ball and accurately kick it into the goal.
We present experimental results that suggest our approach is able to
learn highly effective sensing strategies.
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