Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences


UC Berkeley


Research Areas - Control, Intelligent Systems, and Robotics (CIR)


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.


  • Learning and Optimization:

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

  • Robotics

    All UC Berkeley Robotics Main Page. Automation. Autonomous vehicles. Robots that augment humans. Biomimetic flying and crawling robotics. Gripping and manipulation (macro, micro, and nanoscale). Rapid prototyping of millirobots. Brain machine interfaces. Control of mechanical systems. Model predictive control. Bio-inspired robotics. Robot learning. Tele-immersion. Vehicle dynamics.

  • 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.

  • Robotics at Berkeley

    UCB Robotics Facebook page

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