Overseas Applicants

The following project topics are open to all applicants including Overseas.

For other topics open to EPSRC-Eligible/EU applicants only see here.  Contact Anne Murphy if you have any questions.

Robots Safe and Secure by Construction

Project number: 
400007
Verified implementation of machine-learning components of autonomous systems
Dr. Ekaterina Komendantskaya
Heriot-Watt University

Robotic applications spread to a variety of application domains, from autonomous cars and drones to domestic robots and  personal devices. Each application domain comes with a rich set of requirements such as legal policies, safety and security standards, company values, or simply public perception. They must be realised as verifiable properties of software and hardware. Consider the following policy: a self-driving car must never break the highway code.

Vision based Mobile Autonomy through Object-Level Scene Understanding and Robust Visual SLAM

Project number: 
600005
The goal of this project is to develop vision based algorithms for long-term mobile autonomy in dynamic environments, leveraging object-level scene understanding, multi-sensor fusion and visual SLAM.
Dr. Sen Wang
Heriot-Watt University

AnKobot is sponsoring an exciting PhD project in the field of mobile autonomy using visual Simultaneous Localization and Mapping (SLAM), semantic scene understanding and computer vision.

Robust and Explainable Machine Learning for FinTech Applications

Project number: 
230022
To develop and compare Gaussian Process models with Deep Neural Networks to provide explainable and quantifiable Machine Learning for FinTech applications.
Prof. Mike Chantler
Heriot-Watt University

Deep Neural Network (DNN) technologies coupled with GPU type hardware provide practical methods for learning complex functions from vast datasets.  However, their architectures are often developed using trial and error approaches and the resulting systems normally provide ‘black box’ solutions containing many millions of learnt but abstract parameters. They are therefore extremely difficult to interpret and understand, and their accuracy and certainty of prediction, or classification, are normally not known.

Controllable neural text generation for safe human-machine interactions

Project number: 
400002
The goal of this research is to develop novel neural text generation models, which can guarantee semantic completeness and thus enable safe human-machine interactions.
Prof. Verena Rieser
Heriot-Watt University

Natural Language Generation (NLG) is the task of translating machine-readable representations and data into human language, and thus vital for accountability in safe human-machine collaboration. Neural Network architectures for NLG are promising since they able to capture linguistic knowledge through latent representations using raw input data, and hence have the benefit of simplifying the design of systems by avoiding costly manual engineering of features, with the potential of more easily scaling to new data and domains.

Accountable Conversational AI for Human-Robot Collaboration

Project number: 
240008
To extend & develop models for (Visually) Grounded Human-Machine Collaboration in conversation, including mechanisms for detection & resolution of ambiguity & vagueness to mitigate risk of miscommunication.
Dr. Arash Eshghi
Heriot-Watt University

Ambiguity & Vagueness are pervasive in human  conversation; and their detection and resolution via clarificational dialogue is key to collaborative task success. State-of-the-art HRI cannot handle this, thus increasing risk of miscommunication in human-machine collaboration, especially in safety-critical environments.

Safe Human-Robot Teaming through User-Adaptive Interaction

Project number: 
240007
The goal of this project is to enable robots to safely and effectively collaborate with humans in teams through human-robot interaction by adapting to the social and communication skills level of the user.
Dr. Thusha Rajendran
Heriot-Watt University

For humans to collaborate effectively and safely on shared tasks in human-machine teams, they need to be able to develop a trusting, working relationship. To do this, partners need to have a mutual understanding of the task and understand what the other party can and can’t do. This is known as the ‘Double Empathy Problem’ in autism research (Milton, 2012).

Combining epistemic planning and belief-based programming for collaborative task planning in stochastic domains

Project number: 
230020
To combine state-of-the-art epistemic planning with belief-based stochastic programming techniques for use in collaborative task planning scenarios
Dr. Ron Petrick
Heriot-Watt University

This project will combine advances from epistemic planning (embodied in planners such as PKS) with high-level programming constructs enabling the representation of beliefs and stochastic information (such as the ALLEGRO language) for the purpose of collaborative task planning.

DNN/tensorflow Visualisation

Project number: 
200024
To develop a highly visual and interactive interface for robot planners
Prof. Mike Chantler
Heriot-Watt University

Practical autonomous and semi-autonomous robots need automated plan generation if they are to achieve even simple missions with minimal human intervention. Such plans need to be communicated interactively, simply and quickly to human supervisors in a way that allows them to rapidly assess performance and risk. This is especially the case for mixed robot/human teams.