Research group

Project students

Visiting academics

Alumni

Research

I take the view that progress in robotics and neuroscience is now sufficiently advanced that their main research problems overlap. Our lab facilitates knowledge transfer between engineering and biology by actively researching both fields.

Tactile sensing

The development of robust and accurate artificial touch is needed for autonomous robotic systems to interact physically with complex environments, underlying the future robotization of broad areas of manufacturing, food production, healthcare and assisted living. My main interests and experience include:

1) Development of a leading framework for biomimetic active touch that combines tactile perception and control based on the neuroscience of active perception.

2) Pioneering the use of superresolution techniques in robot touch to reach unprecedented levels of accuracy with tactile sensors (typically 50x improvement over other methods).

3) Application of robots with tactile capabilities to examine the mechanisms for perceptual decision making in animals and humans.

Selected publications
N. Lepora. Biomimetic Active touch with tactile fingertips and whiskers. IEEE Transactions on Haptics, 2016.
U. Martinez-Hernandez et al. Active sensorimotor control for tactile exploration. Robotics and Autonomous Systems, 2016.
N. Lepora et al. Tactile superresolution and biomimetic hyperacuity. IEEE Transactions on Robotics, 2015.
N. Lepora et al. Active Bayesian perception for simultaneous object localization and identification. Robotics: Science and Systems, 2013.
N. Lepora et al. Optimal decision-making in mammals: Insights from a robot study. Royal Society Interface, 2012.
See also letters/conference papers in: IJCNN2010, ICRA2012, IROS2012, ICRA2013, IROS2013, IROS2015, RAL/ICRA 2016, ICRA2016.

Tactile sensors and robot hands

Advances in multi-material 3D-printing mean it is possible for individuals to design and fabricate state-of-the-art tactile robot hands and sensors, offering significant cost and performance advantages over commercial devices. My interests include:

1) Design, fabrication and open-sourcing of 3D-printed tactile fingertips.

2) Design fabrication and open-sourcing of 3D-printed robot hands, in particular combining leading designs with our developments in tactile sensors.

Selected publications
B. Ward-Cherrier et al. Tactile manipulation with a TacThumb integrated on the Open-Hand M2 Gripper. IEEE Robotics & Automation Letters, 2016.
N. Lepora, B. Ward-Cherrier. Tactile quality control with biomimetic active touch. IEEE Robotics & Automation Letters, 2016.
N. Lepora, B. Ward-Cherrier. Superresolution with an optical tactile sensor. IROS, 2015.

Biomimetics

Biomimetics forms the foundation for my research program on hardware for tactile sensing and algorithms for perception and control. I am also interested in the field and methodology as a whole, and have authored a review and co-edited several proceedings.

Selected publications
N. Lepora et al. The state of the art in biomimetics. Bioinspiration and Biomimetics, 2013
Lead or co-editor for conference proceedings for Biomimetic & Biohybrid Systems (2012-2016)

Perception, embodied choice and optimal decision making

I am interested in how human and animal perception relates to accounts of decision making from statistics/mathematics, neuroscience, psychology and philosophy. My main interests and experience include:

1) Understanding perception from the perspective that our brain is embodied in our body, and thus that perceptual choice must encompass also the effect of moving to make a decision.

2) Understanding how sensory and motor systems interrelate in perceptual decision making.

3) Advancing statistical and mathematical methods in optimal decision making.

Selected publications
N. Lepora. Threshold Learning for Optimal Decision making. NIPS, 2016.
N. Lepora, G. Pezzulo. Embodied choice: How action influences perceptual decision making. PLoS Computational Biology, 2015.
N. Lepora, K. Gurney. The basal ganglia optimize decision making over general perceptual hypotheses. Neural Computation, 2012.
N. Lepora et al. Optimal decision-making in mammals: Insights from a robot study. Royal Society Interface, 2012.

Systems-level computation neuroscience

I focus on computation models of the function of brain regions involved in perception and motor control, and their testing by behavioural, psychological and robot experiments. My main interests and experience include:

1) Functional models of the basal ganglia at a systems level of description.

2) Functional models of the cerebellum at a systems level of description.

Selected publications
N. Lepora, K. Gurney. The basal ganglia optimize decision making over general perceptual hypotheses. Neural Computation, 2012.
N. Lepora et al. Sensory prediction or motor control? Application of Marr-Albus type models of cerebellar function to classical conditioning. Frontiers in Computational Neuroscience, 2010.

Neural and physiological modelling

I am also interested in biophysically-detailed models of single neurons and physiological systems, and how these models are fitted to and validated against biological data. My main interests and experience include:

1) Fitting and validating conductance-based (Hodgkin-Huxley) neuron models against electrophysiological data, and thus inferring aspects of neuronal function and computation.

2) Detailed modelling of the motor systems involved in enacting perceptual decisions.

Selected publications
N. Lepora et al. Efficient fitting of conductance-based model neurons from somatic current clamp. Journal of Computational Neuroscience, 2012.
N. Lepora et al. A simple electrophysiological method for characterizing passive and active neuronal properties applied to striatal neurons. European Journal of Neuroscience, 2011.
N. Lepora et al. Recruitment in retractor bulbi muscle during eyeblink conditioning: EMG analysis and common-drive model. Journal of Neurophysiology, 2009.
N. Lepora et al. Evidence from retractor bulbi EMG for linearised control of conditioned nictitating membrane responses. Journal of Neurophysiology, 2007.