3 accepted papers
Typical robot controllers assume firm ground, limiting their effectiveness in controlling robots on dynamic platforms such as trucks or ships. To address this limitation, we propose a reinforcement learning framework for robot locomotion on dynamic rigid platforms and a simulation in which 6-DoF dyn
Neural circuits undergo developmental processes which can be influenced by experience. Here we explore a bio-inspired development process to form the connections in a network used for locality sensitive hashing. The network is a simplified model of the insect mushroom body, which has sparse connecti…
Most approaches for optimisation of neural networks are based on variants of back-propagation. This requires the network to be time invariant and differentiable; neural networks with dynamics are thus generally outside the scope of these methods. Biological neural circuits are highly dynamic yet cle…