EWareNet: Emotion Aware Human Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation
This work addresses the problem of safe and socially compliant robot navigation among pedestrians by incorporating emotion and intent awareness, which is an incremental improvement for social robotics researchers.
This paper introduces EWareNet, a social robot navigation algorithm that predicts pedestrian intent from gait sequences using a transformer-based model. The algorithm then uses this intent, along with pedestrian pose and affect, to dynamically adjust obstacle profiles for mapless navigation, outperforming current state-of-the-art algorithms for intent prediction from 3D gaits.
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.