As AI based decision-making methods make their way from internet applications to more safety-critical physical systems, questions about the robustness of the models and
Explaining the science
The work addresses a number of different scientific questions.
The project is developing techniques for interactive learning, going from rich cross-modal data obtained as a human expert performs various tasks of interest, to the underlying
New techniques are being developed for interrogating the properties of the models being learned. By working with hybrid systems representations of planning and control tasks,
The project is also developing methods for synthesising policies for action, through well understood paradigms. These include model predictive control and dynamic programming or
This project will have two kinds of outcomes.
Firstly, a suite of software tools aimed at interactively learning specifications and activity models, which will in turn be used for synthesising policies for action
Secondly, this suite of tools will be developed in the context of autonomous robots being deployed as surgical assistants in operating rooms. From the outset, the project researchers are working
In this project, the development of methods and software tools is tightly coupled with their continual application in use cases drawn from the application domain of surgical
Experimental work is carried out in a newly established living lab within the Bayes Centre at the University of Edinburgh, which provides a representative environment
It's envisioned that the experiments being carried out in this project as a first step towards larger scale future experiments in situ, to demonstrate the usefulness of these methods in
Equally, it's envisioned that the suite of tools and software will be useful more broadly in a variety of safety-critical applications of robots and autonomous systems.
There is an acknowledged sensitivity around the introduction of automated technologies in a medical domain. Some will be worried about the implications of transferring some forms of
Learning and Autonomy within the School of Informatics at Edinburgh & Turing Fellow
S.D.S. Marín, D. Gomez-Vargas, N. Céspedes, M. Múnera, F. Roberti, P. Barria, S. Ramamoorthy, M. Becker, R. Carelli, C. A. Cifuentes, Expectations and perceptions of healthcare professionals for robot deployment in hospital environments during the COVID-19 pandemic, Research topic: Robotics, Autonomous Systems and AI for Nonurgent/Nonemergent Healthcare Delivery During and After the COVID-19 Pandemic, Frontiers in Robotics and AI - Biomedical Robotics, 2021.
C. Innes, S. Ramamoorthy, ProbRobScene: A probabilistic specification language for 3D robotic manipulation environments, In Proc. IEEE International Conference on Robotics and Automation (ICRA), 2021. Code repository
M. Burke, K. Subr, S. Ramamoorthy, Action sequencing using visual permutations, IEEE Robotics and Automation Letters,
Y. Hristov, S. Ramamoorthy, Learning from demonstration with weakly supervised disentanglement, In Proc.
E. Kahembwe, S. Ramamoorthy, Lower dimensional kernels for video discriminators, Neural Networks Journal, Special
D. Angelov, Y. Hristov, S. Ramamoorthy, From demonstrations to task-space specifications. Using causal analysis to
D. Angelov, Y. Hristov, M. Burke, S. Ramamoorthy, Composing diverse policies for temporally extended tasks, IEEE
M. Asenov, M. Burke, D. Angelov, T. Davchev, K. Subr, S. Ramamoorthy, Vid2Param: Modelling of dynamics parameters
C. Innes, S. Ramamoorthy, Elaborating on learned demonstrations with temporal logic specifications, Robotics: Science
A. Straizys, M. Burke, S. Ramamoorthy, Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based
M. Asenov, N. Zotev, S. Ramamoorthy, A. Kirrander, Inversion of ultrafast X-ray scattering with dynamics constraints, In
M. Burke, Y. Hristov, S. Ramamoorthy, Hybrid system identification using switching density networks, Conference on
Y. Hristov, D. Angelov, A.Lascarides, M. Burke, S. Ramamoorthy, Disentangled Relational Representations for Explaining
M. Burke, S. Penkov, S. Ramamoorthy, From explanation to synthesis: Compositional program induction for learning
D. Angelov, Y. Hristov, S. Ramamoorthy, Using causal analysis to learn specifications from task demonstrations, In Proc.
D. Angelov, Y. Hristov, S. Ramamoorthy, DynoPlan: Combining Motion Planning and Deep Neural Network based
P. Ardón, M. Cabrera, È. Pairet, R. Petrick, S. Ramamoorthy, K. Lohan, M. Cakmak, Affordance-aware handovers with