Spiking Neural Networks for Early Prediction in Human Robot Collaboration

07/29/2018
by   Tian Zhou, et al.
0

This paper introduces the Turn-Taking Spiking Neural Network (TTSNet), which is a cognitive model to perform early turn-taking prediction about human or agent's intentions. The TTSNet framework relies on implicit and explicit multimodal communication cues (physical, neurological and physiological) to be able to predict when the turn-taking event will occur in a robust and unambiguous fashion. To test the theories proposed, the TTSNet framework was implemented on an assistant robotic nurse, which predicts surgeon's turn-taking intentions and delivers surgical instruments accordingly. Experiments were conducted to evaluate TTSNet's performance in early turn-taking prediction. It was found to reach a F1 score of 0.683 given 10 score of 0.852 at 50 performance outperformed multiple state-of-the-art algorithms, and surpassed human performance when limited partial observation is given (< 40 turn-taking prediction capability would allow robots to perform collaborative actions proactively, in order to facilitate collaboration and increase team efficiency.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset