This project is funded by InnovateUK and in collaboration with Sirius Constellation Ltd. The ambitious goal of this project is to create an intelligent situational awareness platform, in the domain of real-time satellite data, where the operator will be able to understand why the platform has drawn a particular conclusion or recommended response. This project will develop the use of explainable planning for use with near-real-time situational awareness information, demonstrating the commercial and operational benefits.

Period: 2018-2019 Partners: Sirius Constellation Ltd

The goal of this project is to advance the state-of-the-art in trustworthy human-AI decision-support systems. The Trust in Human-Machine Partnerhsip (ThUMP) project will address the technical challenges involved in creating explainable AI (XAI) systems so that people using the system can understand the rationale behind and trust suggestions made by the AI. ThUMP will also investigate the legal and ethical challenges involved in instantiating an XAI system for solving resource allocation problems in critical domains, based on varied data from multiple sources, with different levels of reliability and completeness. This project is done in collaboration with three project partners: Save the Children and Schlumberger that provided use cases for the project, and the law firm Hogan Lovells that will cooperate in investigating the legal implications of enhancing machines with transparency and explanations, and how this affects liability and accountability of machines and shared responsibilities.

Period: 2019-2022 Partners: Save the Children and Schlumberger

Clutter in an open world is a challenge for many aspects of robotic systems, especially for autonomous robots deployed in unstructured domestic settings, affecting navigation, manipulation, vision, human robot interaction and planning. SQUIRREL addresses these issues by actively controlling clutter and incrementally learning to extend the robot's capabilities while doing so. We term this the B3 (bit by bit) approach, as the robot tackles clutter one bit at a time and also extends its knowledge continuously as new bits of information become available. SQUIRREL is inspired by a user driven scenario, that exhibits all the rich complexity required to convincingly drive research, but allows tractable solutions with high potential for exploitation. We propose a toy cleaning scenario, where a robot learns to collect toys scattered in loose clumps or tangled heaps on the floor in a child's room, and to stow them in designated target locations.

Period: 2014-2018