  Attention control is a key cognitive ability for humans to select information
relevant to the current task. This paper develops a computational model of
attention and an algorithm for attention-based probabilistic planning in Markov
decision processes. In attention-based planning, the robot decides to be in
different attention modes. An attention mode corresponds to a subset of state
variables monitored by the robot. By switching between different attention
modes, the robot actively perceives task-relevant information to reduce the
cost of information acquisition and processing, while achieving near-optimal
task performance. Though planning with attention-based active perception
inevitably introduces partial observations, a partially observable MDP
formulation makes the problem computational expensive to solve. Instead, our
proposed method employs a hierarchical planning framework in which the robot
determines what to pay attention to and for how long the attention should be
sustained before shifting to other information sources. During the attention
sustaining phase, the robot carries out a sub-policy, computed from an
abstraction of the original MDP given the current attention. We use an example
where a robot is tasked to capture a set of intruders in a stochastic
gridworld. The experimental results show that the proposed method enables
information- and computation-efficient optimal planning in stochastic
environments.
