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 Haptic Languages Information Maximizing Clinical Diagnostics Reactive Learning in Underwater Exploration Robot-Assisted Rehabilitation Software-Enabled Biomedical Devices Vibrissal Responsive Neurons

Control for Nonlinear and Hybrid Systems

Victoria
Tree
Sphere

This project is developing computationally efficient optimal and semi-optimal control and estimation methods. The research is based on the prior development of sequential action control, a numerical iterative optimal control technique that often finds near-optimizers in a single iteration. Using this technique as a starting point, we are interested in developing numerical techniques that can run in real-time for high dimensional nonlinear systems with provable guarantees of stability and near optimality. The key starting point for the work is that sequential action control is a very simple control approach, only relying on a single control vector calculation at any given time, suggesting that near optimal solutions may be very simple to compute. Goals for the research include theoretical guarantees, numerical methods, and experiments. Thus far we have been able to show that the existence of these optimizers depends on local nonlinear controllability. Moreover, keeping track of the computations as one goes leads to stability guarantees. Lastly, some of the geometric ideas generated in this work have produced very efficient methods for pose graph optimization, often an important component of simulataneous localization and mapping (SLAM) algorithms.

People

Taosha Fan (Ph.D. Student)
Giorgos Mamakoukas (Ph.D. Student)

Publications

Generalized proximal methods for pose graph optimization
T. Fan and T. D. Murphey
International Symposium on Robotics Research (ISRR), 2019.

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

Feedback synthesis for underactuated systems using sequential second-order needle variations
G. Mamakoukas, M. Maciver, and T. D. Murphey
International Journal of Robotics Research, vol. 37, no. 13-14, pp. 1826–1853, 2019. PDF

Efficient computation of higher-order variational integrators in robotic simulation and trajectory optimization
T. Fan, J. Schultz, and T. D. Murphey
Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018. PDF, Appendix, Video

Superlinear convergence using controls based on second-order needle variations
G. Mamakoukas, M. MacIver, and T. D. Murphey
IEEE Int. Conf. on Decision and Control (CDC), pp. 4301–4308, 2018. PDF

Feedback synthesis for controllable underactuated systems using sequential second order actions
G. Mamakoukas, M. MacIver, and T. D. Murphey
Robotics: Science and Systems Proceedings, 2017. PDF, Video

Decentralized and recursive identification for cooperative manipulation of unknown rigid body with local measurements
T. Fan, H. Weng, and T. D. Murphey
IEEE Int. Conf. on Decision and Control (CDC), pp. 2842–2849, 2017. PDF

Online feedback control for input-saturated robotic systems on Lie groups
T. Fan and T. D. Murphey
Robotics: Science and Systems Proceedings, 2016. PDF

Structured linearization of discrete mechanical systems on Lie groups: a synthesis of analysis and control
T. Fan and T. D. Murphey
IEEE Int. Conf. on Decision and Control (CDC), pp. 1092 – 1099, 2015. PDF

Funding

This project is funded by the National Science Foundation–Civil and Mechanical Systems: Stability and Optimality Properties of Sequential Action Control for Nonlinear and Hybrid Systems.