This research will address the design and evaluation of safe and trustworthy collaborative robots in assistive scenarios. 

Robotics and autonomous systems (RAS) are emerging as disruptive technologies with the potential to provide personalised and cost effective support for a range of care-related tasks for people with disabilities, including the provision of physical and social assistance and physiotherapy. However, in order to ensure real-world deployment and commercialisation, application-focussed research into safe Human-Robot Interaction, Hazard Analysis and Risk Assessment in a range of dynamic environments and scenarios of use is needed. 

This imperative for these areas of research is particularly significant, given the vulnerability of the end-users interacting with these systems – giving rise to a range of very complex safety and reliability issues and concerns. It is necessary to carefully and deeply consider the safety of assistive robots at not just an operational and functional level – but also from human factors and clinical efficacy perspectives. 

The research will start with a user-centred approach to identifying safety-related issues of specific assistive robots to scope the requirements for real-world use assistive robots by people with different accessibility needs and contexts. As part of this you will also need to review the existing methods and approaches for safety assurance for these systems, with a view to exploring critical barriers to assurance and regulation. Through user-based testing and evaluation using existing assistive robotic platforms, you will analyse the adequacy of current guidelines and standards for assistive robots and identify gaps in the standards using a set of real-world use cases.

Based on your skills and interest, there are several routes you can also consider for this PhD, from the development and validation of hybrid implicit and explicit human-AI mechanisms for generating safe behaviours and conducting hazard analysis and risk assessment, to considering human-factors and psychology related issues that can impact safe interaction, or even experimenting with new forms of embodiment-based social signalling.

Prospective PhD applicants are expected to have a degree in Engineering, Computer Science or Maths with knowledge of Data Science, Machine Learning and AI. Applicants with a background in human-factors and psychology are also welcome. This project will require excellent programming skills with evidence of proficient working knowledge in one or more of the following: C++, C, Java, Python, ROS. 

Supervisors: Profs Praminda Caleb-Solly (School of Computer Science), Carl Macrae (Professor of Organisational Behaviour and Psychology)

For further details and to arrange an interview please contact Prof Praminda Caleb-Solly.

Physical activities such as walking, exercise and dance are not only good for physical well-being, but also mental well-being, particularly when done together with someone. 

This research will explore how robotics technology-based mobility devices could be designed and used to connect people in different remote locations to mediate and coordinate physical interaction between them for physical activities such as exercise, walking and dance.

There are number of avenues to consider in this research, such as exploring most intuitive, effective and engaging ways for people to coordinate their movement-based activities via their assistive robots when they are not in the same physical space, investigating what kind of personalisation methods and input/output modalities are useful to improve the interaction between humans through the robotics technology-based mobility devices and enable long term adaptation to changing needs, or in what ways are interactions affected by people's accessibility needs, their cultures, communities and the interaction environments, or what are suitable embodiments or form-factors for such devices.

This PhD project will benefit from a strong multidisciplinary approach at the interface of Computer Science, Robotics, and Physiotherapy and Dance. Applicants are expected to develop technological advancements in AI and Interaction Design, including using machine-learning for generating personalised user models for children and adults, adaptive motion planning in social environments, feedback generation. In addition, the successful student will design, conduct and analyse experiments to investigate the socio-psychological effects of the technologies. 

Supervisors: Praminda Caleb-Solly and Paul Tennent

For further details and to arrange an interview please contact Prof. Praminda Caleb-Solly. 

Partner: Screen South https://screensouth.org/ 

Telepresence robots offer a significant digital opportunity for people to remotely access social, work and cultural spaces, autonomously moving around them, giving a feeling of connection and presence. As such, telepresence robots can be a transformative tool in enabling engagement with museums and galleries, making connections and improving wellbeing. For a number of disabled people, and those shielding due to lowered immunity due to long-term conditions, having the choice to access cultural spaces and interact with people and objects through telepresence robots, can offer more freedom and flexibility to be ‘present’ in locations.  

However, the interfaces to control telepresence robots can be cumbersome and inaccessible, particularly for those with sensory and/or physical impairments, making it difficult or impossible for them to use these effectively. We are also interested in exploring how by combining telepresence robots with other digital devises, such as VR and haptics, we can enable truly immersive multisensory experiences that are accessible to a variety of participants. 

The aim of this research is to co-design and test a range of different input and output devices and modalities to develop multisensory interfaces that will enable accessible, smooth and enjoyable control and remote interaction. You will explore the integration and use of speech, head and ear-switches, electromyograms, and gaze, amongst other modalities, for control, and visual, haptic and aural modalities for feedback of information to enable rich and creative experiences of the remote space, people and objects. You will study and develop metrics for evaluating usability and user experience for accessible teleoperation using these modalities and custom devices, as well as developing a best practice framework to support future accessible design. The research will also offer the opportunity to draw on disability studies research to understand the lived experience of using telepresence in different contexts, understanding impact on self-efficacy, identity, social relationships and agency in interactions. This research offers several technical and non-technical strands to explore, based on the candidate’s background, skills and experience.

For further details and to arrange an interview please contact Prof. Praminda Caleb-Solly. 

Partner: Queen’s Medical Centre, University Hospital, Nottingham

In busy clinical environments, particularly where patients have a high-level of staff dependency, providing support for clinical staff to improve patient monitoring, triage and management can not only help to ease level of staff stress, but also potentially improve patient safety. This research will investigate how information to assist with clinical decision making can be presented through creative ambient and/or augmented information displays and the impact that different modes and modalities have on user cognitive load, attention and efficiency. This research is situated in the use of tangible devices, and ambient and augmented reality displays, exploring topics in information visualisation, sensory substitution, human factors and user experience design. Considering the context of high-pressured environments, such as dementia wards, you will begin the research with a qualitative observational study, scoping the requirements using co-design with clinical and care professionals, before designing, developing and evaluating a range of approaches for representing the required information.

Based on the candidate’s academic background, skills and experience, the research focus can be either on developing intelligent sensing to capture and represent the key information required for decision-making, or design and development of the approaches for displaying it through different means and modalities, or a combination of both.

For further details and to arrange an interview please contact Prof. Praminda Caleb-Solly. 

Partner: Robotics For Good CIC https://www.roboticsforgood.co.uk/ 

Assistive technologies, such as smart home environments, integrated sensors and service robotics are recognised as emerging tools in helping people with long-term conditions improve their quality of life and live independently for longer. A key aspect of the research into assistive robotics for assisted living is developing contextual and social intelligence for the robot to interact appropriately, safely, and reliably in real-time. This research relates to developing assistive robot behaviour by incorporating both environmental and user data, and behaviour, as part of an overall intelligent control system architecture.

In addition to having a ‘memory’ of previous interactions and situations, assistive robots need access to information that is current and one that provides a dynamic world view of the user (including their emotional state) so that they can provide information and responses that are contextually appropriate. Typical activities for which support can be provided is support with rehabilitation, medication management, cognitive and social stimulation, nutrition management etc. Drawing on information from environmental and activity sensors instrumented into a smart home, and information about the user’s current physical and emotional state, assistive robots can potentially create value through provision of interventions that are more socially intelligent regarding how, and what advice and support they provide. To create a more holistic service, that takes into consideration prioritisation of events based on aspects of health and social circumstance requires an adaptable, intelligent learning system. Building on existing research on intelligent control system architectures, the aim of this research will be to design and test modular semantic memory architectures that can be adapted over time. You will investigate optimal combinations of contextual data comprising implicit (emotional, physiological) and explicit user data (interaction), as well as behavioural activity data assimilated from a range of wearable and smart home sensors, to develop adaptive, intelligent and emotionally engaging robot behaviour to support independent living. 

Fully funded PhD Studentships in Human-Robot Interaction 

• • We have two fully funded PhD studentships for talented candidates to join us from the 1st of October 2024.  

  • You will recieve an annual tax-free stipend based on the UKRI rate plus fully-funded PhD tuition fees for the four years (Home/UK students only) 

  • The students will work with the Boston Dynamics Spot robot to solve real life inspection problems in extreme and factory scenarios. We will develop incremental learning methodologies to develop context-based policies, not only for navigation, but error recovery in long term automation. Human-in-the-loop and teleoperated control methods will be used as the backbone strategy to ensure increasing levels of autonomy during inspection. We will look at human-robot interaction methodologies for the day-to-day operation of the Boston Dynamics Spot mobile inspection robot.

  • The two projects will be in collaboration with RACE (https://race.ukaea.uk/) and Reckitt (https://www.reckitt.com/)

This PhD project will develop on the Nottingham Robotic Mobility Assistant, NoRMA (https://github.com/HCRLabRepo/NoRMA) to study triadic learning methodologies for developing effective assistance policies for wheelchair users to support their day to day activities.

This PhD project will be in collaboration with RACE (https://race.ukaea.uk/) and aims to develop incremental learning methodologies to develop context-based policies, not only for navigation, but error recovery in long term automation. Human-in-the-loop and teleoperated control methods will be used as the backbone strategy to ensure increasing levels of autonomy during inspection. We will look at human-robot interaction methodologies for efficient management and optimisation of parallel tasks encountered in day-to-day operation of the Boston Dynamics Spot mobile inspection robot.

This PhD studentship will investigate trust from a theory of mind point of view to model a robot’s trustworthiness from the perspective of a human, and vice versa.

Lifelong learning with robotic vacuum cleaners in social spaces: In collaboration with Beko Plc. (https://www.bekoplc.com/), this PhD project will focus on these challenges by targeting multiple strands of research in perception, planning, human-in-the-loop learning, and shared control for service robots. The ability to detect and recover from errors during navigation is an essential ability for an autonomous service robot that can run for extended periods of time. In addition, functioning in human settings, these robots should be programmed to adhere to social cues in a context- dependent manner, not only to enable safe, but also acceptable functionality.

Human-robot interaction (HRI) is a multidisciplinary field that studies how humans and robots can communicate and collaborate effectively and naturally. Gesture recognition is one of the key components of HRI, as it enables humans to use intuitive and expressive body motions to convey commands, intentions, and emotions to robots. However, most of the existing gesture recognition methods rely on vision-based sensors, such as cameras, that have limitations in terms of occlusion, illumination, privacy, and computational cost.

The aim of this PhD project is to develop novel methods for inertial sensor-based gesture recognition for HRI.

The project will involve the following objectives:

• Review the state-of-the-art methods and challenges of inertial sensor-based gesture recognition for HRI.

• Develop new methods for gesture segmentation, classification, and generation using inertial sensors, such as accelerometers and gyroscopes, worn on the human body.

• Evaluate the performance and usability of the proposed methods on various HRI scenarios and tasks, such as navigation, manipulation, social interaction, and entertainment.

• Investigate the human factors and ethical issues of using inertial sensors for gesture recognition for HRI.

The successful candidate will have a strong background in computer science, engineering, or mathematics, with good programming skills in Python or C++, and good knowledge of machine learning. Experience in inertial sensor data processing, machine learning, or human-robot interaction is desirable but not essential. The candidate will be supervised by Dr. Lucas Fonseca and Prof Praminda Caleb-Solly  from the School of Computer Science, and will have access to the state-of-the-art facilities and resources of the CHART research group.

Human-machine interfaces (HMIs) are systems that enable humans to interact with machines, such as computers, robots, or assistive devices, using various modalities, such as speech, touch, or gesture. HMIs have many applications, such as entertainment, education, health, and industry. However, most of the existing HMIs are based on small and predefined set of actions, which limit the naturalness and expressiveness of the human-machine interaction. In addition, they often don't consider the user's limitations.

The aim of this PhD project is to explore the use of human movement as a strategy for designing and evaluating novel HMIs that can adapt to the user’s preferences, context, and goals. 

The project will involve the following objectives:

• Review the state-of-the-art methods and challenges of using human movement for HMIs.

• Develop new methods for capturing, analyzing, and synthesizing human movement data using various sensors, such as inertial sensors, motion capture systems, or cameras.

• Design and implement novel HMIs that use human movement as an input or output modality for various tasks and domains, such as gaming, education, or rehabilitation.

• Evaluate the usability and user experience of the proposed HMIs using quantitative and qualitative methods.

The successful candidate will have a strong background in computer science, engineering, or design, with good programming skills in Python or C++. Experience in human movement analysis, machine learning, or human-computer interaction is desirable but not essential. The candidate will be supervised by Dr. Lucas Fonseca and Prof Praminda Caleb-Solly  from the School of Computer Science, and will have access to the state-of-the-art facilities and resources of the CHART research group.