54 citations · 211 across the 34 of their papers we have counts for
6 papers · 1 filter
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates
Tatiana Lopez-Guevara, Michael Burke, Nicholas K. Taylor +1
Model-free reinforcement learning (RL) is a powerful tool to learn a broad range of robot skills and policies. However, a lack of policy interpretability can inhibit their successf…
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
Todor Davchev, Kevin Sebastian Luck, Michael Burke +3
Manipulation skills involving contact and friction are inherent to many robotics tasks. Using the class of motor primitives for peg-in-hole like insertions, we study how robots can…
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…
NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces
Miguel Jaques, Michael Burke, Timothy Hospedales
Learning low-dimensional latent state space dynamics models has been a powerful paradigm for enabling vision-based planning and learning for control. We introduce a latent dynamics…
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…
Black-Box Saliency Map Generation Using Bayesian Optimisation
Mamuku Mokuwe, Michael Burke, Anna Sergeevna Bosman
Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches…