4 citations · 8 across the 7 of their papers we have counts for
7 papers · 1 filter
Action sequencing using visual permutations
Michael Burke, Kartic Subr, Subramanian Ramamoorthy
Humans can easily reason about the sequence of high level actions needed to complete tasks, but it is particularly difficult to instil this ability in robots trained from relativel…
Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification
Artūras Straižys, Michael Burke, Subramanian Ramamoorthy
Precision cutting of soft-tissue remains a challenging problem in robotics, due to the complex and unpredictable mechanical behaviour of tissue under manipulation. Here, we conside…
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…
Hybrid system identification using switching density networks
Michael Burke, Yordan Hristov, Subramanian Ramamoorthy
Behaviour cloning is a commonly used strategy for imitation learning and can be extremely effective in constrained domains. However, in cases where the dynamics of an environment m…