The Taub Faculty of Computer Science Events and Talks

This talk will describe new active learning processes for automated modeling of dynamical systems across a number of disciplines. One of the long-standing challenges in robotics is achieving robust and adaptive performance under uncertainty. One approach to adaptive behavior is based on self-modeling, where a system continuously evolves multiple simulators of itself in order to make useful predictions. The robot is rewarded for actions that cause disagreement among predictions of different candidate simulators, thereby elucidating uncertainties. The concept of self modeling will then be generalized to other systems, demonstrating how analytical invariants can be derived automatically for physical systems purely from observation. Application to modeling physical and biological systems will be shown.