Adaptation and Learning in Robots; Principles of Biological Control
Professor Slotine is the Director of the Nonlinear Systems Laboratory which studies general mathematical principles of nonlinear system stability, adaptation, and learning, and how they apply to robots and to models of biological control. The lab is particularly interested in how stability and performance constraints shape system architecture, representation, and algorithms in robots, and in whether similar constraints may in some cases lead to similar mechanisms in biological systems. Tools from nonlinear control, such as sliding variables, wave variables, and contraction theory also suggest a number of simple models of physiological motor control, which may help understand the specific roles of hierarchies, motor primitives, and nerve transmission delays.
Current projects include:
Fast motion-vision coordination in robots; robotic catching of free-flying objects
Models of the cerebellum and stability of biological feedback loops under nerve transmission delays
Adaptive multiresolution approximation networks for real-time control and prediction
Stable control using motion primitives; performance of combinations of local and centralized control mechanisms
Entrainment models in mechanical and biological systems
Nonlinear observer design techniques for real-time brain imaging
9.110J Nonlinear Control System Design
Rutishauser, U., Slotine, J.J.E., and Douglas, R., "Computation in Dynamically Bounded Asymmetric Systems," PLoS Computational Biology, 11(1), 2015.
Bonnabel, S., and Slotine J.J.E., "A Contraction Theory-Based Analysis of the Stability of the Deterministic Extended Kalman Filter," I.E.E.E. Transactions on Automatic Control, 60(2), 2015.
Zhao, C., Wang, W.X., Liu, Y.Y., and Slotine, J.J.E., "Intrinsic Dynamics Induce Global symmetry in Network Controllability," Scientific Reports, 5, 2015.
Manchester, I.R., Slotine, J.J.E., "Transverse Contraction Criteria for Existence, Stability, and Robustness of a Limit Cycle," Systems & Control Letters, 63, 2014.