  Motivated by the goal of endowing robots with a means for focusing attention
in order to operate reliably in complex, uncertain, and time-varying
environments, we consider how a robot can (i) determine which portions of its
environment to pay attention to at any given point in time, (ii) infer changes
in context (e.g., task or environment dynamics), and (iii) switch its attention
accordingly. In this work, we tackle these questions by modeling context
switches in a time-varying Markov decision process (MDP) framework. We utilize
the theory of bisimulation-based state abstractions in order to synthesize
mechanisms for paying attention to context-relevant information. We then
present an algorithm based on Bayesian inference for detecting changes in the
robot's context (task or environment dynamics) as it operates online, and use
this to trigger switches between different abstraction-based attention
mechanisms. Our approach is demonstrated on two examples: (i) an illustrative
discrete-state tracking problem, and (ii) a continuous-state tracking problem
implemented on a quadrupedal hardware platform. These examples demonstrate the
ability of our approach to detect context switches online and robustly ignore
task-irrelevant distractors by paying attention to context-relevant
information.
