University of Edinburgh

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:

University: 
University of Edinburgh
University: 
University of Edinburgh
University: 
University of Edinburgh
University: 
University of Edinburgh