Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN



+44 (0)20 7594 6300y.demiris Website




1014Electrical EngineeringSouth Kensington Campus





Publication Type

182 results found

Wang R, Amadori PV, Demiris Y, Real-Time Workload Classification during Driving using HyperNetworks

Classifying human cognitive states from behavioral and physiological signalsis a challenging problem with important applications in robotics. The problemis challenging due to the data variability among individual users, and sensorartefacts. In this work, we propose an end-to-end framework for real-timecognitive workload classification with mixture Hyper Long Short Term MemoryNetworks, a novel variant of HyperNetworks. Evaluating the proposed approach onan eye-gaze pattern dataset collected from simulated driving scenarios ofdifferent cognitive demands, we show that the proposed framework outperformsprevious baseline methods and achieves 83.9\% precision and 87.8\% recallduring test. We also demonstrate the merit of our proposed architecture byshowing improved performance over other LSTM-based methods.


Cully A, Demiris Y, Hierarchical Behavioral Repertoires with Unsupervised Descriptors

Enabling artificial agents to automatically learn complex, versatile andhigh-performing behaviors is a long-lasting challenge. This paper presents astep in this direction with hierarchical behavioral repertoires that stackseveral behavioral repertoires to generate sophisticated behaviors. Eachrepertoire of this architecture uses the lower repertoires to create complexbehaviors as sequences of simpler ones, while only the lowest repertoiredirectly controls the agent's movements. This paper also introduces a novelapproach to automatically define behavioral descriptors thanks to anunsupervised neural network that organizes the produced high-level behaviors.The experiments show that the proposed architecture enables a robot to learnhow to draw digits in an unsupervised manner after having learned to draw linesand arcs. Compared to traditional behavioral repertoires, the proposedarchitecture reduces the dimensionality of the optimization problems by ordersof magnitude and provides behaviors with a twice better fitness. Moreimportantly, it enables the transfer of knowledge between robots: ahierarchical repertoire evolved for a robotic arm to draw digits can betransferred to a humanoid robot by simply changing the lowest layer of thehierarchy. This enables the humanoid to draw digits although it has never beentrained for this task.


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