Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration

Tian Zhou; Juan P. Wachs

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arXiv · 1709.09276

Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration

Tian Zhou·Juan P. Wachs

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Abstract

Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40% of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.

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BibTeX RIS

Tian Zhou, Juan P. Wachs. 2017-09-26. Early Turn-taking Prediction with Spiking Neural Networks for Human Robot Collaboration. https://arxiv.org/abs/1709.09276

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