Robotic Detection of a Human-Comprehensible Gestural Language for Underwater Multi-Human-Robot Collaboration
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In this paper, we present a motion-based robotic communication framework that enables non-verbal communication among autonomous underwater vehicles (AUVs) and human divers. We design a gestural language for AUV-to-AUV communication which can be easily understood by divers observing the conversation unlike typical radio frequency, light, or audio based AUV communication. To allow AUVs to visually understand a gesture from another AUV, we propose a deep network (RRCommNet) which exploits a self-attention mechanism to learn to recognize each message by extracting maximally discriminative spatio-temporal features. We train this network on diverse simulated and real-world data. Our experimental evaluations, both in simulation and in closed-water robot trials, demonstrate that the proposed RRCommNet architecture is able to decipher gesture-based messages with an average accuracy of 88-94 73-83 performing a message transcription study with human participants, we also show that the proposed language can be understood by humans, with an overall transcription accuracy of 88 RRCommNet on embedded GPU hardware, for real-time use on board AUVs in the field.
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