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Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...
Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurement ...
The slogan “robots will pervade our environment” has become a reality. Drones and ground robots are used for commercial purposes while semi-autonomous driving systems are standard accessories to traditional cars. However, while our eyes have been riveted o ...
Humans have a remarkable way of learning, adapting and mastering new manipulation
tasks. With the current advances in Machine Learning (ML), the promise of having
robots with such capabilities seems to be on the cusp of reality. Transferring human-level
sk ...
Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...
This doctoral thesis navigates the complex landscape of motion coordination and formation control within teams of rotary-wing Micro Aerial Vehicles (MAVs). Prompted by the intricate demands of real-world applications such as search and rescue or surveillan ...
Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...
Robot motion planning involves finding a feasible path for a robot to follow while satisfying a set of constraints and optimizing an objective function. This problem is critical for enabling robots to navigate and perform tasks in realworld environments. H ...
Small variance asymptotics is emerging as a useful technique for inference in large scale Bayesian non-parametric mixture models. This paper analyses the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small va ...
The development of robots that can dance has received considerable attention. However, they are often either limited to a pre-defined set of movements and music or demonstrate little variance when reacting to external stimuli, such as microphone or camera ...