Research projects

CONTACT-AI aims to transform legged robots into contact-rich explorers, where legs are used to walk but also to cautiously manipulate the environment to resolve ambiguities in visual perception and navigate unknown terrain.

Analysis of Bayesian Intelligent Autonomous Systems focuses on the information-theoretic basis of Active Inference. What are the best data points for an agents? How optimal is data collection based on minimizing expected free energy?

Design of an intelligent autonomous system for quadrupedal robot locomotion using Active Inference (AIF). AIF is a neuroscience-based framework of perception and action, and could bring us closer to how animals learn to walk.

Computation in biological brain tissue consumes several orders of magnitude less power than silicon-based systems. Motivated by this fact, this project aims to develop the world’s first hybrid neuro-in-silico Artificial Intelligence (AI) computer.

Sequential Domain Adaptive Intelligent Systems focuses on domain-adaptative classification over an ordered sequence of biased samples. An example of such a sequence is medical data from hospital along a geographic path.

Domain Adaptive Pattern Recognition explores the limits of generalization for a special case of statistical learning where training data and test data are differently biased samples of some underlying data-generating distribution.