52 citations · 81 across the 15 of their papers we have counts for
16 papers
Physics-informed reinforcement learning via probabilistic co-adjustment functions
Nat Wannawas, A. Aldo Faisal
Reinforcement learning of real-world tasks is very data inefficient, and extensive simulation-based modelling has become the dominant approach for training systems. However, in hum…
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Paul Festor, Giulia Luise, Matthieu Komorowski +1
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, aut…
Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling
Nat Wannawas, Mahendran Subramanian, A. Aldo Faisal
Functional Electrical Stimulation (FES) can restore motion to a paralysed person's muscles. Yet, control stimulating many muscles to restore the practical function of entire limbs…
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation
Nat Wannawas, Ali Shafti, A. Aldo Faisal
Human movement disorders or paralysis lead to the loss of control of muscle activation and thus motor control. Functional Electrical Stimulation (FES) is an established and safe te…
Non-invasive Cognitive-level Human Interfacing for the Robotic Restoration of Reaching & Grasping
Ali Shafti, A. Aldo Faisal
Assistive and Wearable Robotics have the potential to support humans with different types of motor impairments to become independent and fulfil their activities of daily living suc…
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions
Petros Christodoulou, Robert Tjarko Lange, Ali Shafti +1
From a young age humans learn to use grammatical principles to hierarchically combine words into sentences. Action grammars is the parallel idea, that there is an underlying set of…