  We present EWareNet, a novel intent and affect-aware social robot navigation
algorithm among pedestrians. Our approach predicts the trajectory-based
pedestrian intent from gait sequence, which is then used for intent-guided
navigation taking into account social and proxemic constraints. We propose a
transformer-based model that works on commodity RGB-D cameras mounted onto a
moving robot. Our intent prediction routine is integrated into a mapless
navigation scheme and makes no assumptions about the environment of pedestrian
motion. Our navigation scheme consists of a novel obstacle profile
representation methodology that is dynamically adjusted based on the pedestrian
pose, intent, and affect. The navigation scheme is based on a reinforcement
learning algorithm that takes pedestrian intent and robot's impact on
pedestrian intent into consideration, in addition to the environmental
configuration. We outperform current state-of-art algorithms for intent
prediction from 3D gaits.
