We use theoretical, computational and experimental studies to investigate the computational principles underlying skilled motor behaviour. Our focus is on the control of the hand and arm as a model system that demonstrates many of the features which make sensorimotor control hard. The upper limb has a large number of interacting degrees of freedom and it interacts with many different objects under a variety of environmental conditions. Despite this complexity, healthy humans demonstrate a remarkable ability to generate accurate and appropriate motor behaviour when interacting with the world. To examine the computations underlying sensorimotor control, we have developed a research programme that uses computational techniques from machine learning, control theory and signal processing together with novel experimental techniques that include robotic interfaces and virtual reality systems that allow for precise experimental control over sensory inputs and task variables. There are four broad areas to our research programme:

  • Motor Planning and optimal control
  • Probabilistic models of sensorimotor control
  • Predictive models for estimation, control & sensory processing
  • Modular approaches to kinematic and dynamic motor learning
  • Robotic interface development

Lab Support

Best viewed with Firefox 3, Safari 4 or Explorer 8. 2009 Computational & Biological Learning Lab, Department of Engineering, University of Cambridge