  Object-goal navigation (Object-nav) entails searching, recognizing and
navigating to a target object. Object-nav has been extensively studied by the
Embodied-AI community, but most solutions are often restricted to considering
static objects (e.g., television, fridge, etc.). We propose a modular framework
for object-nav that is able to efficiently search indoor environments for not
just static objects but also movable objects (e.g. fruits, glasses, phones,
etc.) that frequently change their positions due to human intervention. Our
contextual-bandit agent efficiently explores the environment by showing
optimism in the face of uncertainty and learns a model of the likelihood of
spotting different objects from each navigable location. The likelihoods are
used as rewards in a weighted minimum latency solver to deduce a trajectory for
the robot. We evaluate our algorithms in two simulated environments and a
real-world setting, to demonstrate high sample efficiency and reliability.
