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

TL;DR

This paper introduces the Cognitive Turn-taking Model (CTTM) using Spiking Neural Networks to predict human turn-taking intentions early in collaborative tasks, enhancing robot-human teamwork efficiency.

Contribution

The paper presents a novel cognitive model leveraging SNNs for early turn-taking prediction, outperforming existing algorithms and humans with partial communication cues.

Findings

CTTM outperforms state-of-the-art algorithms.

CTTM surpasses human prediction with partial cues.

Early turn-taking prediction improves robot-human collaboration.

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.