Biography

Kathryn Kasmarik is a Professor of Computer Science at the University of New South Wales, Australian Defence Force Academy (UNSW Canberra). Kathryn completed a Bachelor of Computer Science and Technology at the University of Sydney, including a study exchange at the University of California, Los Angeles (UCLA). She graduated with First Class Honours and the University Medal in 2002. She completed a PhD in Computer Science through the National ICT Australia and the University of Sydney in 2007. She moved to UNSW Canberra in 2008. Kathryn’s research interests lie in the area of autonomous mental development for computers and robots. Her speciality in this area is computational models of motivation, such as curiosity, interest, achievement, affiliation and power motivation. She has published over 130 articles on these topics in peer reviewed conference and journals, and two books. Her research has been funded by the Australian Research Council and Defence Science and Technology Group, among other sources. She was the Deputy Head of School (Teaching) for the School of Engineering and IT at UNSW Canberra from 2018-2021 and is currently Secretary of the IEEE Australian Capital Territory Section.

Talk

Autonomous Bootstrapping of Collective Motion Behaviours for Swarming Robots

Collective behaviours such as swarm formations of autonomous agents offer the advantages of efficient movement, redundancy, and potential for human guidance of a single swarm organism. However, with the explosion in hardware platforms for autonomous vehicles, swarm robotic programming requires significant manual input for each new platform. This talk introduces two developmental approaches to evolving collective behaviours whereby the developmental process is guided by a task-non-specific value system. Two value systems will be considered: the first based on a survey of human perception of swarming and the second based on a computational model of curiosity. Unlike traditional approaches, these value systems do not need in advance the precise characteristics of the intended swarming behaviours. Rather they reward the emergence of structured collective motions, permitting the emergence of multiple collective behaviours, including aggregation and navigation behaviours. This talk will examine the performance of these value systems in a series of controlled experiments on point-mass ‘boids’ and simulated robots. We will see how the value systems can recognise multiple “interesting” structured collective behaviours and distinguish them from random movement patterns. We will also see how the value systems can be used to tune random motions into structured collective motions.