54 citations · 211 across the 34 of their papers we have counts for
20 papers · 1 filter
Behavioural Quality Is Not Learning Utility: Objective Mismatch in Human Behaviour Models for Robot Learning
Haoyang Jiang, Dana Kulic, Michael Burke +1
Human behaviour models are increasingly used to provide simulated human interactions for training robots via reinforcement learning (RL). Existing approaches primarily evaluate the…
A Probabilistic Model for Skill Acquisition with Switching Latent Feedback Controllers
Juyan Zhang, Dana Kulic, Michael Burke
Manipulation tasks often consist of subtasks, each representing a distinct skill. Mastering these skills is essential for robots, as it enhances their autonomy, efficiency, adaptab…
Influence-Based Reward Modulation for Implicit Communication in Human-Robot Interaction
Haoyang Jiang, Elizabeth A. Croft, Michael G. Burke
Communication is essential for successful interaction. In human-robot interaction, implicit communication holds the potential to enhance robots' understanding of human needs, emoti…
Generating robotic elliptical excisions with human-like tool-tissue interactions
Arturas Straizys, Michael Burke, Subramanian Ramamoorthy
In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, lit…
Rendering Stable Features Improves Sampling-Based Localisation with Neural Radiance Fields
Boxuan Zhang, Lindsay Kleeman, Michael Burke
Neural radiance fields (NeRFs) are a powerful tool for implicit scene representations, allowing for differentiable rendering and the ability to make predictions about unseen viewpo…
Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations
Zhongliang Jiang, Yuan Bi, Mingchuan Zhou +3
Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real-time and radiation-free. However, due to inter…
Motion Perceiver: Real-Time Occupancy Forecasting for Embedded Systems
Bryce Ferenczi, Michael Burke, Tom Drummond
This work introduces a novel and adaptable architecture designed for real-time occupancy forecasting that outperforms existing state-of-the-art models on the Waymo Open Motion Data…
Social Cue Detection and Analysis Using Transfer Entropy
Haoyang Jiang, Elizabeth A. Croft, Michael G. Burke
Robots that work close to humans need to understand and use social cues to act in a socially acceptable manner. Social cues are a form of communication (i.e., information flow) bet…
Adapting Neural Models with Sequential Monte Carlo Dropout
Pamela Carreno-Medrano, Dana Kulić, Michael Burke
The ability to adapt to changing environments and settings is essential for robots acting in dynamic and unstructured environments or working alongside humans with varied abilities…
Learning robotic cutting from demonstration: Non-holonomic DMPs using the Udwadia-Kalaba method
Artūras Straižys, Michael Burke, Subramanian Ramamoorthy
Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manip…
Multi-Step First: A Lightweight Deep Reinforcement Learning Strategy for Robust Continuous Control with Partial Observability
Lingheng Meng, Rob Gorbet, Michael Burke +1
Deep Reinforcement Learning (DRL) has made considerable advances in simulated and physical robot control tasks, especially when problems admit a fully observed Markov Decision Proc…
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…
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…
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…
From explanation to synthesis: Compositional program induction for learning from demonstration
Michael Burke, Svetlin Penkov, Subramanian Ramamoorthy
Hybrid systems are a compact and natural mechanism with which to address problems in robotics. This work introduces an approach to learning hybrid systems from demonstrations, with…