Research Interests:
Intention prediction and human-robot interaction, Eye
tracking and EEG sensing, Computational topology for
modelling spatiotemporal phenomena
Research Description: My work addresses the problem of predicting the activity of people by combining models obtained from, e.g., eye tracking (or even wearable EEG sensors), with robotic perception. This allows us to obtain contextual information which is key for human-robot co-work applications. A particularly unique aspect of my work is the use of novel computational methods, e.g., based on topology, that allows us to abstract over the spatiotemporal processes associated with these sensors.
Publications:
S. Penkov, A. Bordallo, S. Ramamoorthy, Physical symbol grounding and instance learning through demonstration and eye tracking, IEEE International Conference on Robotics and Automation, 2017.
Preprint, Video, Bibtex
S. Penkov, A. Bordallo, S. Ramamoorthy, Inverse eye tracking for intention inference and symbol grounding in human-robot collaboration, Robotics: Science and Systems Workshop on Planning for Human-Robot Interaction, 2016.
Preprint, Publisher's link, Bibtex
Research Description: My work addresses the problem of predicting the activity of people by combining models obtained from, e.g., eye tracking (or even wearable EEG sensors), with robotic perception. This allows us to obtain contextual information which is key for human-robot co-work applications. A particularly unique aspect of my work is the use of novel computational methods, e.g., based on topology, that allows us to abstract over the spatiotemporal processes associated with these sensors.
Publications:
Preprint, Video, Bibtex
Preprint, Publisher's link, Bibtex