We address a state-of-the-art reinforcement learning (RL) control approach to automatically configure robotic prosthesis impedance parameters to enable end-to-end, continuous locomotion intended for transfemoral amputee subjects. Specifically, our actor-critic based RL provides tracking control of a robotic knee prosthesis to mimic the intact knee profile. This is a significant advance from our previous RL based automatic tuning of prosthesis control parameters which have centered on regulation control with a designer prescribed robotic knee profile as the target. In addition to presenting the complete tracking control algorithm based on direct heuristic dynamic programming (dHDP), we provide an analytical framework for the tracking controller with constrained inputs. We show that our proposed tracking control possesses several important properties, such as weight convergence of the learning networks, Bellman (sub)optimality of the cost-to-go value function and control input, and practical stability of the human-robot system under input constraint. We further provide a systematic simulation of the proposed tracking control using a realistic human-robot system simulator, the OpenSim, to emulate how the dHDP enables level ground walking, walking on different terrains and at different paces. These results show that our proposed dHDP based tracking control is not only theoretically suitable, but also practically useful.

A new technique could reduce the time and discomfort of adjusting to a new prosthetic knee. A collaboration between researchers from North Carolina State University, the University of North Carolina and Arizona State University has resulted in a new technique that enables more rapid "tuning" of powered prosthetic knees, allowing patients to comfortably walk with a new prosthetic device in minutes, rather than hours after the device is first fitted After receiving the prosthetic knee, the device is tuned to tweak 12 different control parameters to accommodate the specific patient and address prosthesis dynamics like joint stiffness throughout the entire gait cycle. Traditionally, a practitioner works directly with the user to modify a handful of parameters in a process that could take several hours. However, by using a computer program that utilizes reinforcement learning--a type of machine learning--to modify all 12 parameters simultaneously, the new system allows patients to use their powered prosthetic knee to walk on a level surface after approximately 10 minutes of use. "We begin by giving a patient a powered prosthetic knee with a randomly selected set of parameters," Helen Huang, co-author of a paper on the work and a professor in the Joint Department of Biomedical Engineering at NC State and UNC, said in a statement.

A researcher at the University of the West of England (UWE Bristol) is developing a bio-inspired'smart' knee joint for prosthetic lower limbs. Dr Appolinaire Etoundi, based at Bristol Robotics Laboratory, is leading the research and will analyse the functions, features and mechanisms of the human knee in order to translate this information into a new bio-inspired procedure for designing prosthetics. Dr Etoundi gained his PhD in bio-inspired technologies from the University of Bristol where he developed a design procedure for humanoid robotic knee joints. He is now turning his attention to nature, a growing area in robotics known as Bio-mimicry, combining curiosity about how biological systems work with solving complex engineering problems, in order to develop a prototype smart knee joint for prosthetics. Andy Lewis, a Paralympic Triathlon Gold Medallist (Rio 2016), who wears a lower limb prosthetic, will try out the new joint once developed, to compare its energy consumption and gait efficiency to current prosthetics.