7 citations · 9 across the 4 of their papers we have counts for
5 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…
Towards AI-controlled FES-restoration of movements: Learning cycling stimulation pattern with reinforcement learning
Nat Wannawas, A. Aldo Faisal
Functional electrical stimulation (FES) has been increasingly integrated with other rehabilitation devices, including robots. FES cycling is one of the common FES applications in r…
Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES
Nat Wannawas, Ali Shafti, A. Aldo Faisal
Functional Electrical Stimulation (FES) is a technique to evoke muscle contraction through low-energy electrical signals. FES can animate paralysed limbs. Yet, an open challenge re…
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…