11 citations · 19 across the 6 of their papers we have counts for
7 papers
From Demonstrations to Task-Space Specifications: Using Causal Analysis to Extract Rule Parameterization from Demonstrations
Daniel Angelov, Yordan Hristov, Subramanian Ramamoorthy
Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work, we show that i…
Learning rewards for robotic ultrasound scanning using probabilistic temporal ranking
Michael Burke, Katie Lu, Daniel Angelov +4
Informative path-planning is a well established approach to visual-servoing and active viewpoint selection in robotics, but typically assumes that a suitable cost function or goal…
Disentangled Relational Representations for Explaining and Learning from Demonstration
Yordan Hristov, Daniel Angelov, Michael Burke +2
Learning from demonstration is an effective method for human users to instruct desired robot behaviour. However, for most non-trivial tasks of practical interest, efficient learnin…
Composing Diverse Policies for Temporally Extended Tasks
Daniel Angelov, Yordan Hristov, Michael Burke +1
Robot control policies for temporally extended and sequenced tasks are often characterized by discontinuous switches between different local dynamics. These change-points are often…
Vid2Param: Modelling of Dynamics Parameters from Video
Martin Asenov, Michael Burke, Daniel Angelov +3
Videos provide a rich source of information, but it is generally hard to extract dynamical parameters of interest. Inferring those parameters from a video stream would be beneficia…
DynoPlan: Combining Motion Planning and Deep Neural Network based Controllers for Safe HRL
Daniel Angelov, Yordan Hristov, Subramanian Ramamoorthy
Many realistic robotics tasks are best solved compositionally, through control architectures that sequentially invoke primitives and achieve error correction through the use of loo…
Using Causal Analysis to Learn Specifications from Task Demonstrations
Daniel Angelov, Yordan Hristov, Subramanian Ramamoorthy
Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work we show that it…