  Humans are known to construct cognitive maps of their everyday surroundings
using a variety of perceptual inputs. As such, when a human is asked for
directions to a particular location, their wayfinding capability in converting
this cognitive map into directional instructions is challenged. Owing to
spatial anxiety, the language used in the spoken instructions can be vague and
often unclear. To account for this unreliability in navigational guidance, we
propose a novel Deep Reinforcement Learning (DRL) based trust-driven robot
navigation algorithm that learns humans' trustworthiness to perform a language
guided navigation task. Our approach seeks to answer the question as to whether
a robot can trust a human's navigational guidance or not. To this end, we look
at training a policy that learns to navigate towards a goal location using only
trustworthy human guidance, driven by its own robot trust metric. We look at
quantifying various affective features from language-based instructions and
incorporate them into our policy's observation space in the form of a human
trust metric. We utilize both these trust metrics into an optimal cognitive
reasoning scheme that decides when and when not to trust the given guidance.
Our results show that the learned policy can navigate the environment in an
optimal, time-efficient manner as opposed to an explorative approach that
performs the same task. We showcase the efficacy of our results both in
simulation and a real-world environment.
