University of Edinburgh

Teaching Robots to Plan

Project number: 
124026
To develop and implement methods for instructing robots directly through natural language, where the instructions refer to temporally extended plans executed on physical robots (e.g., object manipulation)
Dr. Subramanian Ramamoorthy
University of Edinburgh

The vast majority of applications of robots do not involve just one isolated task (such as grasping an object) but instead carefully choregraphed sequences of such tasks, with dependencies between tasks not just in terms of what comes after what but also how the previous task should be performed (in a quantitative sense at the level of motor control) in order to set up for the next. Moreover, there are numerous subjective variables in these tasks, e.g., how close should it come to a delicate object or how hard should it pull on a cable?

Learning Dexterous Robotic Manipulation

Project number: 
124025
Learning autonomous grasping and manipulation skills that are safe to be deployed in human environment with data-efficient deep reinforcement learning and human-robot skill transfer
Dr. Zhibin Li
University of Edinburgh

Background

A large variety of robotic applications strongly involve handling various objects as the core process for task completion. To date, most of these jobs are still performed by people. Although some are automated by robots, those solutions primarily rely on pre-designed rules or tele-operation (limited operational time due to cognitive overload), which unavoidably limits the performance in changing environments. This project consists of multiple challenging research topics in robotic manipulation.

Project description

University: 
University of Edinburgh

Endoscopic Robot for Distal Lung Sampling

Project number: 
240011
Develop image-guided control algorithms for autonomous pulmonary sampling using a robotic endoscopic platform.
Dr. Mohsen Khadem
University of Edinburgh

Pulmonary infiltrates such as pus, blood, or protein, which lingers within the parenchyma of the lungs are the leading cause of pneumonia, tuberculosis.  Pulmonary infiltrates in mechanically ventilated (MV) critically ill patients in the intensive care unit (ICU) are a major diagnostic challenge and due to the poor sampling methods available.

Human-Machine Collaboration for Efficient Spatio-Temporal Biodiversity Monitoring

Project number: 
230021
Efficient estimation of changes in spatio-temporal distributions of wildlife via active data collection
Dr. Oisin Mac Aodha
University of Edinburgh

There is a critical need for robust and accurate tools to scale up biodiversity monitoring and to manage the impact of anthropogenic change. For example, the monitoring of individual species that are particularly sensitive to habitat conversion and climate change can act as an important indicator of ecosystem health. Existing approaches for collecting data on individual species in the wild have traditionally been based on manual surveys performed by human experts.

Deep Learning of Object Shape from Video

Project number: 
400006
Learning visual representations of objects that encode both shape and appearance
Dr. Oisin Mac Aodha
University of Edinburgh

The shape and 3D structure of the world provides us with rich signal that enables us to interact with objects and to navigate in novel and dynamic environments. Despite the importance of this information to human visual reasoning it still remains largely underutilized in modern deep learning based semantic image understanding pipelines commonly used in robotics. For example, current best performing approaches for object classification in images are predominantly based on heavily supervised feedforward convolutional neural networks.

Variable Stiffness Actuation for Bioinspired Underwater Propulsion

Project number: 
140029
This project aims to study the use of Variable Stiffness Actuators (VSA) embedded in aquatic propulsors to ensure persistent operation at maximum propulsive efficiency.
Dr. Francesco Giorgio-Serchi
University of Edinburgh

Fish and other aquatic organisms propel themselves via flapping foil. Similarly, aquatic organisms such as squids and octopuses perform pulsation of a hollow, flexible chamber of their body in order to recursively ingest and expel fluid and in this way perform a pulsed-jetting locomotion routine. In order to enhance swimming efficiency, many aquatic organisms exploit resonance-based phenomena where activation frequency and natural frequency of the system (combined fluid and body) are matched.

Autonomous Driving in Urban Environments

Project number: 
100017
Develop and evaluate algorithms for autonomous driving in urban environments
Dr. Stefano Albrecht
University of Edinburgh

The coming decades will see the creation of fully autonomous vehicles (AVs) capable of driving without human intervention. Among the expected benefits of AVs are a significant reduction in traffic incidents, congestion, and pollution, while dramatically improving cost-efficiency.

University: 
University of Edinburgh