Our research group seeks to understand how complex behaviour arises from relatively simple nervous systems using a bio-robotic methodology. That is we strive to embed hypothesis into computational and robot models and compare the resultant behaviour to that of animals in the same settings. The process of instantiating conceptual hypothesis in a real-world system aims to avoid over-simplifying problems through unrealistic assumptions, or being misled by over-fitting to hand-crafted simulated environments.
We take insects (ants, bees, fruit-flies) as our model species as they display a wide range of complex behaviours (foraging, communication, visual navigation, nest building, learning) that vastly exceed the capabilities of current robots, but using a nervous system that is sufficiently simple that we hope to understand it’s function. Much of our research is focussed on behaviours that can be abstracted to robot applications (e.g. GPS-free visual navigation), or can be used in industrial settings (pesticide free pest control).
By revealing the secrets of complex behaviour in insects, we hope to enhance our understanding of natural systems and allow development of similarly capable artificial systems.
Elias Lattish (PhD Candidate - 2nd supervisor)
Raymond Kirk (PhD Candidate - 2nd supervisor)
Xuelong Sun (PhD Candidate - 2nd supervisor)