University of Edinburgh

Explaining and interpretable task planning

Project number: 
230005
Study how to generate explanations for robotic behavior and how to unify the goals of the robot with Human intentions
Dr. Vaishak Belle
University of Edinburgh

The issue of explanations for AI systems cooperating with humans has been a topic of considerable interest of late. But it is widely argued that current solutions that are based on local representations do not fully capture the reasoning behind the underlying decision.

Understanding referential communication through the lens of child-robot interaction

Project number: 
200015
We would like to understand and devise efficient mechanisms for referential communication understanding in robots, through the lens of experiments with children’s referential communication to a robot
Dr. Subramanian Ramamoorthy
University of Edinburgh

Referential communication refers to how an agent communicates their intended meaning to another agent about a referent, or object. This is a crucial aspect of human-robot interaction, especially when the robot must collaborate with human users in order to help them perform a shared task.

MiniMat

Project number: 
200012
Developing a minimal animal/animate soft robot for use in animal cruelty prevention and animal-assisted interventions with vulnerable children
Dr. J. Michael Herrmann
University of Edinburgh

The aim of the ‘MiniMat’ project is to develop a minimal animal-like soft robot and test its impact on vulnerable children in cruelty prevention and animal-assisted interventions (AAI). Intentional animal cruelty begins as young as 4 years of age, often by highly vulnerable children. MiniMat is required because of the ethical issues of using live animals in interventions with children who have harmed animals.  Similarly, animal-assisted interventions can benefit vulnerable children’s mental health.

Bi-Manual Inverted Robotic Manipulation

Project number: 
140023
To develop and evaluate safe skills for autonomous robotic manipulation by two cooperating inverted (ceiling mounted) UR10 robots.
Prof. Robert Fisher FIAPR FBMVA
University of Edinburgh

Background

In real world applications, a large variety of jobs involve handling various objects as the core process. However, most of these jobs nowadays are still performed by people. One obvious fact of human manipulation is the use of two hands that cooperate to achieve some task. But not too much research has been done involving coordination and optimal use of two hands. At the system level, having two hands naturally introduces physical redundancy that provides extra layer of safety and reliability against malfunctions such as object slippage.

Bridging Together Compliant and Continuum Robots

Project number: 
140009
Exploit control principles employed in flexible-joint and compliant robots to develop a more rigorous basis for the control of continuum soft systems
Dr. Francesco Giorgio-Serchi
University of Edinburgh

Research Background of the Project: Soft manipulators have recently gained significant recognition for their versatility and advantages over rigid-link systems. Their multi-DOF bending capabilities and their intrinsic compliance make them a viable technology for complex tasks where an enhanced degree of flexibility is required as well as locomotion tools for bioinspired robots. However, the complexity of these continuum actuators makes them hard to control and, consequently, less suited for everyday use in industrial, medical or other robotics applications.

Learning cross-modal models of surgical skill

Project number: 
120010
We would like to develop models of surgical skill, based on rich cross-modal data obtained from surgical training kits and other laboratory based mockups, for the purposes of quantifying these skills as well as enabling synthesis of similar behaviours in robots
Dr. Subramanian Ramamoorthy
University of Edinburgh

There is significant interest in characterising surgical skills in the form of detailed models that correlate hand movements, applied forces and the context of the shape and texture of tissues that are actually being manipulated.

Unsupervised Learning of Objects in Motion

Project number: 
140026
The goal of this project is to develop systems that can learn to segment out object from videos with very little or no supervision at all.
Dr. Laura Sevilla-Lara
University of Edinburgh

Objects play a central role in the behaviour of intelligent systems like robots. This makes semantic object segmentation a fundamental basic component of many applications. State-of-the-art object segmentation is often done training expensive networks with lots of labelled images. This means that new knowledge is expensive to acquire, since the network needs lots of labelled images of each new object. In addition, these networks take as input only single images, ignoring all the useful information from the  motion of objects.

Fluidic Control for Soft Robotic Systems in Extreme Environments

Project number: 
100007
To develop integrated soft robotic systems which have fluidic, rather than electronic, control for safe operation in extreme environments.
Dr. Adam Stokes
University of Edinburgh

The ignition of flammable liquids and gases in offshore oil and gas environments is a major risk and can cause loss of life, serious injury, and significant damage to infrastructure. Power supplies that are used to provide regulated voltages to drive motors, relays, and power electronic controls can produce heat and cause sparks. As a result, the European Union requires ATEX certification on electrical equipment to ensure safety in such extreme environments. Implementing designs that meet this standard is time-consuming and adds to the cost of operations.

Connect-R: industrial-scale self-building modular robotics

Project number: 
100006
To work as part of the Connect-R team in developing a large-scale (>10 cubic metres), self-building modular robotic system.
Dr. Adam Stokes
University of Edinburgh

Operations in hostile environments–such as those found in Nuclear Decommissioning, Oil and Gas, Mining, and Space–require the execution of sophisticated tasks. Examples of these tasks are: building structures, and deploying tools for inspection, lifting, and cutting. These environments pose a significant risk to the health and safety of manual workers. The safety of personnel can be mitigated by the use of autonomous robotic systems which can perform the required tasks in these extreme environments.

These environments present the following challenges: