  We present ProxEmo, a novel end-to-end emotion prediction algorithm for
socially aware robot navigation among pedestrians. Our approach predicts the
perceived emotions of a pedestrian from walking gaits, which is then used for
emotion-guided navigation taking into account social and proxemic constraints.
To classify emotions, we propose a multi-view skeleton graph convolution-based
model that works on a commodity camera mounted onto a moving robot. Our emotion
recognition is integrated into a mapless navigation scheme and makes no
assumptions about the environment of pedestrian motion. It achieves a mean
average emotion prediction precision of 82.47% on the Emotion-Gait benchmark
dataset. We outperform current state-of-art algorithms for emotion recognition
from 3D gaits. We highlight its benefits in terms of navigation in indoor
scenes using a Clearpath Jackal robot.
