Surprise-Based Learning (SBL)

Surprise-based learning is capable of providing a physical robot the ability to autonomously learn and plan in an unknown environment without any prior knowledge of its actions or their impact on the environment. This is achieved by creating a model of the environment using prediction rules. A prediction rule describes the observations of the environment prior to the execution of an action and the forecasted or predicted observation of the environment after the action. The algorithm learns by investigating 'surprises', which are inconsistencies between the predictions and observed outcome. SBL has been successfully demonstrated on a modular robot learning and navigating in an office-environment, and other real-world applications below..

US Air Force Office of Scientific Research News (2006)


BOOK (1994)

Demonstration videos

Previous Work on SBL

Nadeesha Ranasinghe, PhD Thesis: Learning to Detect and Adapt to Unpredicted Changes, Computer Science Department, Viterbi School of Engineering, University of Southern California, August 2012. Download

Nadeesha Ranasinghe and Wei-Min Shen. Autonomous Surveillance Tolerant to Interference. In Intl. Conf. on Towards Autonomous Robotic Systems, Bristol, UK, August 2012.
[347.1kB pdf] 

Bo Ryu, Nadeesha Ranasinghe, Wei-Min Shen, Kurt Turck, and Michael Muccio. BioAIM: Bio-inspired Autonomous Infrastructure Monitoring. In Proc. 2015 IEEE Intl. Conf. on Military Communications, Tampa, FL, October 2015.
[503.0kB pdf] 

Nadeesha Ranasinghe and Wei-Min Shen. Surprise-based developmental learning and experimental results on robots. In icdl-09, Shanghai, China, June 2009.
[533.9kB pdf] 

Nadeesha Ranasinghe and Wei-Min Shen. Surprise-Based Learning for Developmental Robotics. In Proc. 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robo\ tic Systems, Edinburgh, Scotland, August 2008.
[310.5kB pdf]