The aim of this project is to enable a team of mobile manipulators with the capability of dexterous simultaneous manipulation and locomotion.
Stipend and Home Fees
Humans perform numerous tasks in their daily life that require collaboration with others. In these scenarios the two humans work together, anticipate how the other will behave and guide their partner towards the joint goal. For instance, two humans can effortlessly move and flip large boxes in a warehouse. Likewise, a human can pour wine into a glass held out by another human, without spilling.
Robots and UAVs are often required to operate (semi)autonomously, especially when there is limited human awareness for safe operation. In such cases, self-navigation functionalities are required. This proposal relates to the adaptation of RF signals of opportunity for navigation using SLAM approaches.
Multi-agent reinforcement learning (MARL) uses reinforcement learning techniques to train a set of agents to solve a specified task. This includes agents working in a team to collaboratively accomplish tasks, as well as agents in competitive scenarios with conflicting interests. Recent advances in MARL have leveraged deep learning to scale to bigger problems and address some of the inherent challenges in MARL .
Digital twins are increasingly becoming a choice, if not a trend to forecast, operate and manage complex systems. However, all digital twins are derived from human defined models about the environment or the physical asset. That is, the human defines the digital proxies, the virtualised environment, the technology, etc. This project questions the potential of machines and/or networks to derive its own digital model and workspace. In particular, the emphasis on systems that are beyond human visual sight and where communication synchronicity is not guaranteed, i.e.
Human-robot interaction requires building a joint understanding of context, facilitating collaboration naturally and seamlessly on tasks, e.g. by joint goal setting, communicating progress or clarifying the user’s intention. To achieve the ability of natural command, control, and feedback in real world scenarios requires the construction of user interaction models supported by spatial modelling and reasoning, that can link a detailed digital landscape to real world concepts.
Critical illness can affect individuals at any age and for a wide range of medical and surgical conditions. Recovery can be prolonged, and complicated by fatigue, impaired attention and limited engagement with rehabilitation for physical and mental health reasons. Socially assistive robots provide an opportunity for bespoke rehabilitation programmes to be developed by health care professionals, then delivered by the robot, from the time of recovery from critical care, through the rest of the inpatient journey, to the transition home.