There will be an IPAB workshop on April 5th from 12:45-14:00 in room 4.31/4.33. Pastries will be available.
Speaker: Tianqi Wei
Title: A Bio-inspired Reinforcement Learning Rule to Optimise Dynamical Neural Networks for Robot Control
Abstract: 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 clearly able to support learning. We propose a reinforcement learning approach inspired by the mechanisms and dynamics of biological synapses. The network weights undergo spontaneous fluctuations, and a reward signal modulates the centre and amplitude of fluctuations to converge to a desired network behaviour. We test the new learning rule on a 2D bipedal walking simulation, using a control system that combines a recurrent neural network, a bio-inspired central pattern generator layer and proportional-integral control, and demonstrate the first successful solution to this benchmark task.
Speaker: Marija Jegorova
Title: GANs Application to Robot Control
Abstract: Current Generative Adversarial Networks (GANs) applications mostly include image or video generation and completion. I will report my recent progress on applying GANs to generating trajectories for a robotic arm (specifically Baxter). Producing a selection of diverse trajectories for throwing to the same set target is potentially applicable to obstacle avoidance.