EWareNet: Emotion Aware Human Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation

TL;DR

EWareNet introduces a transformer-based social navigation system that predicts pedestrian intent from gait sequences and adapts robot navigation dynamically, improving safety and social compliance in pedestrian-rich environments.

Contribution

The paper presents a novel transformer-based intent prediction model integrated with an adaptive spatial profile fusion for social robot navigation, without environmental assumptions.

Findings

Outperforms current state-of-the-art intent prediction algorithms.

Effectively integrates gait-based intent prediction into mapless navigation.

Dynamically adjusts obstacle profiles based on pedestrian behavior.

Abstract

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.