Control Engineering seminars

Generative control policies are often credited with representing multimodal robot actions. This talk tests that explanation through experiments and theory, finding benefits instead associated with iterative computation, training noise and on-manifold biases. The results motivate alternatives that obtain those algorithmic advantages without relying on distribution matching as the explanation.

Diffusion and flow models provide expressive distributions over continuous actions in robotic control. This talk explains how modelling extended action sequences improved imitation learning and subsequently offline and offline-to-online reinforcement learning. It reviews algorithms built around these representations and large-scale control models that use them, connecting policy expressiveness with practical learning performance.

The nervous system is fundamentally a closed loop control device: the output of actions continually influences the internal state and subsequent actions. This is true at the single cell and even the molecular level, where “actions” take the form of signals that are fed back to achieve a variety of functions, including homeostasis, excitability and various kinds of multistability that allow switching and storage of memory. It is also true at the behavioural level, where an animal’s motor actions directly influence sensory input on short timescales, and higher level information about goals and intended actions are continually updated on the basis of current and past actions. Studying the brain in a closed loop setting requires a multidisciplinary approach, leveraging engineering and theory as well as advances in measuring and manipulating the nervous system. I will describe our recent attempts to achieve this fusion of approaches at multiple levels in the nervous system, from synaptic signalling to closed loop brain machine interfaces.

Olivier Guyon explains the physics and engineering of extreme adaptive optics for directly imaging exoplanets. Atmospheric turbulence distorts incoming wavefronts; sensors measure those distortions and deformable mirrors apply compensating corrections. The lecture covers guide stars, fast detectors, actuators, real-time computing and control systems. Extreme adaptive optics requires correction rates above a kilohertz, mirrors with more than a thousand actuators and nanometer-scale measurements of optical aberrations. Guyon describes how integrated systems suppress and calibrate unwanted starlight, including approaches developed with the Subaru Extreme Adaptive Optics system. These advances support efforts to equip extremely large telescopes to image potentially habitable planets.

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.

General anesthesia is a drug-induced, reversible condition comprised of five behavioral states: unconsciousness, amnesia (loss of memory), antinociception (loss of pain sensation), akinesia (immobility), and hemodynamic stability with control of the stress response. Our work shows that a primary mechanism through which anesthetics create these altered states of arousal is by initiating and maintaining highly structured oscillations. These oscillations impair communication among brain regions. We illustrate this effect by presenting findings from our human studies of general anesthesia using high-density EEG recordings and intracranial recordings. These studies have allowed us to give a detailed characterization of the neurophysiology of loss and recovery of consciousness due to propofol. We show how these dynamics change systematically with different anesthetic classes and with age. As a consequence, we have developed a principled, neuroscience-based paradigm for using the EEG to monitor the brain states of patients receiving general anesthesia. We demonstrate that the state of general anesthesia can be rapidly reversed by activating specific brain circuits. Finally, we demonstrate that the state of general anesthesia can be controlled using closed loop feedback control systems. The success of our research has depended critically on tight coupling of experiments, signal processing research and mathematical modeling.