Spatial Reasoning and Planning with Deep Neural Networks

Context & Potential Impact - Performing tasks autonomously in novel environments is a major challenge for many applications of robotics, from indoor robots to autonomous vehicles. In classical robotics, such tasks have been separated into smaller components: localisation, mapping, planning and navigation. Techniques such as Simultaneous Localisation and Mapping (SLAM) have been popular for localisation and mapping, while optimisation, graph search and control theory has been prominent in the field of planning and navigation. While these methods can solve well-specified individual problems, their performances are upper-bounded by the assumptions made by each particular component.
Advances in neural network architectures, together with successful integration of these models into Reinforcement Learning (RL) frameworks, have transformed the field of computer vision and robotics, allowing a learning-based approach to problems traditionally solved by manually implementing insights of domain experts. Learning-based approaches have two major advantages. Firstly, learning-based algorithms can keep improving and adapting to the application domain with more availability of data, whereas manually implemented methods are fixed and do not learn to adapt. Secondly, learning-based methods are capable of automatically discovering inherent regularities and characteristics of the application domain and exploiting them to improve their performances without having such strategies hardcoded.
Aims & Objectives - In this DPhil research project, we develop end-to-end trainable systems for spatial reasoning and planning, where components for localisation, mapping, and planning are purposefully engineered. We hypothesise that having explicit constraints on the capability of the components will allow the system to learn transformations and policies that could generalise to unseen scenarios, and could be easily fine-tuned for new environments and tasks.
Research Method Novelty - The problem of learning-driven autonomous navigation is an open-ended problem, and there isn't yet a system that takes a fully learning-based approach without compromising robustness and interpretability. Many previous works either assume a fully known environment, or use neural network architectures that do not have a guarantee of learning robust internal representations of spatial memory usable for general purposes. Therefore, we focus on integrating a family of memory networks into our system, which should give a more interpretable representation of spatial memory. Having a better state representation would also unload the burden of learning a complex policy at the RL phase. We will also develop an end-to-end differentiable planning algorithm that can work with these intermediate representations so that better representations are learnt with more training data.
Alignment to EPSRC's strategies and research areas - Artificial intelligence technologies, Image and vision computing, Information systems, Robotics and Software engineering

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.