ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Emotion Classification EWALK ProxEmo (ours) Accuracy 82.4 # 1
Emotion Classification EWALK STEP [bhattacharya2019step] Accuracy 78.24 # 2
Emotion Classification EWALK Baseline (Vanilla LSTM) [Ewalk] Accuracy 55.47 # 3