Reinforcement learning offers a promising framework to develop algorithms that can reproduce hard-to-model behaviours in robotics. Recently, there have been many success stories where reinforcement learning has been used to solve problems which were previously considered prohibitively difficult for traditional AI techniques. Unfortunately, it is still not clear how to transfer these methods to robotic systems, where problems involve high-dimensional and continuous state and action spaces, that are also often not completely observable (nor noise-free).
Oliver is interested in investigating how robotic platforms can successfully reason and act in response to noisy sensor readings by learning useful representations of perceptual data. Specifically, he is interested in developing methods which learn to integrate multiple perception modalities, including underused modalities such as contact or force sensing, within reinforcement learning frameworks.
Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Based Interaction
Oliver Limoyo, Trevor Ablett, Filip Marić, Luke Volpatti and Jonathan Kelly