Stroke is a leading cause of disability in the UK, with approximately 50% of stroke survivors being left disabled and dependent [1]. Upper limb impairment is very common post-stroke and limits many activities of daily living, especially those requiring reach to grasp actions such as picking up a drink. Therefore, the development of rehabilitation technologies is essential to help the recovery of upper limb motor function post-stroke. Intensive, goal-orientated practice of movement is vital for recovery of upper limb function [2]. Robotic therapy and functional electrical stimulation (FES) have proved to be effective technologies in reducing upper limb impairment, enabling people with limited physical upper limb ability to practice repeated movements [2, 3]. Effectiveness of therapy is also suggested to improve when associated with the patient’s voluntary intention to move [3]. However, few rehabilitation technologies maximise voluntary effort in therapeutic interventions. This work forms part of an on-going project aimed at developing a rehabilitation system (Stimulation Assistance through Iterative Learning: SAIL) that uses robotic support and FES, mediated by advanced iterative learning control (ILC) algorithms. ILC is a technology transferred from industrial robotics. To correct performance error, after each repetition of the task, ILC uses performance data gathered from previous trials to update the FES signal that is applied during the subsequent trial. ILC encourages voluntary effort by the participant, by providing just enough FES to assist the participant in performing the movement [4]. Recent work has assessed the feasibility of ILC and FES during rehabilitation [4, 5, 6]. These studies investigated functionality following ILC controlled FES applied to the anterior deltoid and triceps during trajectory tracking tasks. Results showed that assisted and unassisted tracking performance and movement outcome scores improved over the course of 18 to 25 training sessions and that the amount of FES required to produce accurate tracking reduced [5, 6]. However, activities of daily living not only involve reaching out towards an object but also grasping and manipulating it. Our current work involves developing the SAIL technology to also stimulate muscles in the hand and wrist to facilitate functional reach to grasp tasks, such as picking up a drink. Preliminary studies to develop a biomechanical model of the arm, together with a model of human movement using motor control principles, have just finished. In these studies fourteen healthy participants, who each completed three functional tasks (turning on a light, closing a drawer and picking up a drink). Kinematic and EMG muscle activity data were collected for the upper limb. The model of the arm will be used to develop the ILC algorithms. Stroke patients will undergo rehabilitative training with the new system in Spring 2012. Clinical and biomechanical outcome measures will assess performance and identify changes in motor impairment during functional reach to grasp tasks. [1] National Audit Office Department of Health. Progress in improving stroke care. 2010. [2] Oujamaa L, Rlave I, Froger J, et al. Rehabilitation of arm function after stroke. Literature review. Annuals Physical Rehabil Med, 52: 269-293, 2009. [3] De Kroon JR, IJzerman MJ, Chae J, et al. Relation between stimulation characteristics and clinical outcome of the upper extremity in stroke. Rehabil Med, 37: 65-74, 2005. [4] Freeman, CT., et al. (2012) Iterative Learning Control in Healthcare: Electrical Stimulation and Robotic-assisted Upper Limb Stroke Rehabilitation. IEEE Control Systems Magazine. [5] Hughes AM, et al. (2009) Feasibility of iterative learning control mediated by functional electrical stimulation for reaching after stroke. Neurorehabil Neural Repair 23: 559-568. [6] Meadmore KL, et al. Iterative Learning Control mediated Function Electrical Stimulation and 3D robotics reduces motor impairment in chronic stroke. 2011, NeuroEng Rehabil. (Revision Under Review).