Interactive & Emergent Autonomy Lab

Projects

Active Learning and Data-Driven Control Active Perception in Human-Swarm Collaboration Algorithmic Matter and Emergent Computation Control for Nonlinear and Hybrid Systems Cyber Physical Systems in Uncertain Environments Information Maximizing Clinical Diagnostics Reactive Learning in Underwater Exploration Robot-Assisted Rehabilitation Software-Enabled Biomedical Devices

Active Learning and Data Driven Control

KL-E^3 for Bayesian Optimization

Robot sampling a state space (for Bayesian optimization) while taking into account dynamic constraints (keeping the cart double pendulum inverted).

Quadcopter Learning

Our method (blue quadcopter) exploring the state-space of the stochastic model next to an information maximizing method (green)


We work on machine learning techniques that use control authority (inputs to the system) to actively learn. The inputs do not simply contribute to learning through action, the inputs are chosen to maximize the amount of learning that occurs in a given amount of time. Much of our recent work has used ergodicity as a principle of information coverage. Moreover, we have been using active learning with respect to information about unknown Koopman operators (nonparametric descriptions of dynamical systems).

People

Giorgos Mamakoukas (Ph.D. Student)

Collaborators

Brenna Argall, Northwestern University
Mitra Hartmann, Northwestern University

Publications

Active learning of dynamics for data-driven control using Koopman operators
I. Abraham and T. D. Murphey
IEEE Transactions on Robotics, 2019. PDF, Code

CPL-Sync: Efficient and guaranteed planar pose graph optimization using the complex number representation
T. Fan, H. Wang, M. Rubenstein, and T. D. Murphey
IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2019. Winner of ABB Best Student Paper Award. PDF

Local Koopman operators for data-driven control of robotic systems
G. Mamakoukas, M. Castano, X. Tan, and T. D. Murphey
Robotics: Science and Systems Proceedings, 2019. PDF

Model-based control using Koopman operators
I. Abraham, G. de la Torre, and T. Murphey
Robotics: Science and Systems Proceedings, 2017. PDF, Code