Our research is focussed on learning, adaptation and control mechanisms needed to improve the skills of autonomous robots, improving their ability to cope with unknown and dynamic environments. Even as we work towards enhancing the autonomy of robots, we want these robots to be intelligent enough to be able to effectively collaborate with human co-workers (to learn from them, to take instructions from them and to coordinate joint actions with them). With the rapid development of such robotic skills, it is becoming increasingly more important that autonomous systems be safe, trustworthy and responsibly developed.

Our approach is to couple AI methods development with the development of complete systems that can meaningfully address realistic applications. Salient application areas of interest include the following.

Dexterous Manipulation Skills: We are fascinated by the richness and expressivity of human/animal manipulation, ranging from the lightness of touch when handling soft materials in our daily activities, to the generality of our ability to plan with novel objects in uncertain environments. We would like to understand these behaviours well enough to be able to devise autonomous robots that can behave similarly. Such skills are needed in many domains, ranging from advanced manufacturing to surgical automation.

Physical Scene Understanding and Active Sensing: The combination of novel sensors mounted on or manipulated by robots, with physics-informed machine learning methods that enable efficient inference of causal, interpretable, multi-scale models, represents fascinating new opportunities for real-time diagnostic systems and their incorporation into closed-loop robotic systems. We are developing this technology with collaborators from medicine, and the bio-physical sciences.

Intention-aware Planning in Human-centred Environments: Prof Ramamoorthy’s work at FiveAI (with a diverse team including a few RAD alumni) has taken several ideas developed within our research group into a technology stack that has been deployed and demonstrated on public roads (e.g., in the StreetWise trials in London). This includes methods for prediction and hypothesis testing in multi-agent situations, and safe motion planning in interactive dynamic environments.

Funding for our research work has come from the following sources (selected grants, listed in reverse chronological order):

• UKRI Grant: Topological Methods for Learning to Steer Self-Organised Growth
• Enabling Advanced Autonomy through Human-AI Collaboration, Alan Turing Institute funded project.
• UKRI Research Node on Trustworthy Autonomous Systems Governance and Regulation
• A Cyber-Physical System for Unified Diagnosis and Treatment of Lung Diseases
• UoE-L&G Advanced Care Research Centre (ACRC): New Technologies of Care
• Trustworthy Control Synthesis: Metrology Fellowship, UK National Physical Laboratory
• MAMMOBOT: A flexible robot for early breast cancer diagnosis, Cancer Research UK
• D-FOCUS: Drone-formation control for countering future unmanned aerial systems, Defence and Security Accelerator (DASA) competition contract
• Safe AI for Surgical Assistance, Alan Turing Institute Fellowship and Sponsored Project
• ORCA HUB: Offshore Robotics for Certification of Assets, UKRI Industrial Strategy Fund
• CDE-DSTL project on Autonomous mapping of airborne and localised contamination (REDALERT)
• DARPA Grant, Explainable AI Program: Common Ground Learning and Explanation (COGLE)
• EC ICT FET Proactive Grant: Hybrid and Diversity-Aware Collective Adaptive Systems (SmartSociety)
• EC FP7-ICT STREP: Topology based Motion Synthesis for Dexterous Manipulation (TOMSY)
• EPSRC Grant: Topology based Motion Synthesis

We are also grateful for generous support from the following organisations: Microsoft Research, Xerox PARC, Dyson, Five AI, Thales Maritime Systems.

The following video clips, prepared for public engagement purposes, provide accessible glimpses into some aspects of our research agenda.