Decoding Perceived Hazardousness from User's Brain States to Shape Human-Robot Interaction


With growing availability of robots and rapid advances in robot autonomy, their proximity to humans and interaction with them continuously increases. In such interaction scenarios, it is often evident what a robot should do, yet unclear how the actions should be performed. Humans in the scene nevertheless have subjective preferences over the range of possible robot policies. Hence, robot policy optimization should incorporate the human’s preferences. One option to gather online information is the decoding of the human’s brain signals. We present ongoing work on decoding the perceived hazardousness of situations based on brain signals from electroencephalography (EEG). Based on experiments with participants watching potentially hazardous traffic situations, we show that such decoding is feasible and propose to extend the approach towards more complex environments such as robotic assistants. Ultimately, we aim to provide a closed-loop system for human-compliant adaptation of robot policies based on the decoding of EEG signals.

Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction