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

arXiv: https://arxiv.org/abs/2011.09438 · HTML: https://mlsanity.com/paper/2011.09438/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [SACSoN: Scalable Autonomous Control for Social Navigation](https://mlsanity.com/paper/2306.01874/) (2023)
- [SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation](https://mlsanity.com/paper/2403.00991/) (2024)
- [Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2403.09793/) (2024)
- [Learning Personalized Human-Aware Robot Navigation Using Virtual Reality Demonstrations from a User Study](https://mlsanity.com/paper/2203.14741/) (2022)
- [Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning](https://mlsanity.com/paper/2409.10655/) (2024)
- [EmoBipedNav: Emotion-aware Social Navigation for Bipedal Robots with Deep Reinforcement Learning](https://mlsanity.com/paper/2503.12538/) (2025)

## Abstract (arXiv)

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.