Control, Intelligent Systems, and Robotics (CIR)

Overview

Control and Robotics at EECS is concerned with the general problem of modeling systems and machines, and then making them respond appropriately to inputs. Optimization and mathematical techniques play a key role, especially as systems of interest grow in scale. Control ranges from applications in semiconductor process control to hybrid and networked control to nonlinear and learning control, and includes interactions with faculty in Mechanical Engineering and Integrative Biology as well as between EE and CS. Robotics is interpreted broadly to include mobile autonomous systems from millimeter-sized mobile robots to 3 meter rotor span helicopters, fixed autonomous systems for assembly, as well as human augmentation capabilities such as telepresence, and virtual reality. Providing robots with image understanding capabilities is one of the key research areas, as well as using computer vision to assist humans.

Topics

• Learning and Optimization:

  Convex optimization. Graphical models. Nonparametric Bayesian methods. Markov decision problems. Financial engineering. Game theory.

• Robotics:

  Autonomous vehicles. Biomimetic flying and crawling robotics. Gripping and manipulation (macro, micro, and nanoscale). Rapid prototyping of millirobots.

• Hybrid systems:

  Network control. Feedback control systems. Nonlinear control. Semiconductor process control. Manufacturing. Distributed control. Sensor network control. Robust control. Intelligent highways. Transportation engineering. Autonomous vehicles. Air traffic control. Control over communication networks.

• Biological systems:

  Bio-mimetic robotics. Neuro-prosthetic systems. Computational motor control. Human-centered control. Man-machine systems. Computer vision. Active and real-time perception. Neuroengineering. Biological systems modeling. Systems biology.

Research Centers

• Biomimetic Millisystems Lab
• Partners for Advanced Transit and Highways (PATH)
• Robotics and Intelligent Machines Laboratory

Faculty

• Elad Alon
• Venkat Anantharam
• Ruzena Bajcsy
• Peter Bartlett
• Thomas F. Budinger
• John F. Canny
• Jose M. Carmena
• Leon O. Chua
• Stephen E. Derenzo
• Laurent El Ghaoui
• Ronald S. Fearing
• Ken Goldberg
• Thomas A. Henzinger
• Michael Jordan
• Jitendra Malik
• Kristofer Pister
• Elijah Polak
• Kameshwar Poolla
• Stuart J. Russell
• Anant Sahai
• Seth R. Sanders
• Alberto L. Sangiovanni-Vincentelli
• S. Shankar Sastry
• Costas J. Spanos
• Claire Tomlin (coordinator)
• Pravin Varaiya
• Martin Wainwright
• Lotfi A. Zadeh

Related Courses

• CS 182. Neural Basis of Thought and Language
• CS 188. Introduction to Artificial Intelligence
• CS 280. Computer Vision
• CS 281A. Statistical Learning Theory
• CS 281B. Advanced Topics in Learning and Decision Making
• CS 287. Advanced Robotics
• CS 289. Knowledge Representation and Use in Computers
• EE 120/120L. Signals and Systems
• EE 125. Introduction to Robotics
• EE 128. Feedback Control Theory
• EE 129. Neural and Nonlinear Information Processing
• EE 192. Mechatronic Design Laboratory
• EE 221A. Linear System Theory
• EE 222. Nonlinear Systems - Analysis, Stability and Control
• EE 223. Stochastic Systems: Estimation and Control
• EE 227A. Introduction to Convex Optimization
• EE 227B. Convex Optimization and Approximation
• EE 245. Introduction to MEMS Design