The proposed research aims at designing robust, real time learning controllers for powered lower limb prosthesis worn by above-knee amputees. It centers on adaptive optimal tuning of prosthetic knee joint impedance parameters with an ultimate goal of achieving human-prosthesis symbiosis. Current state-of-the-art approaches rely on a predetermined collection of knee joint impedance parameters, resulted from tedious manual tuning in a clinic. In addition to a lack of adaptability to different users, current impedance controls do not adapt to different use environments. One of the key design challenge is due to the constant interaction between the human user and the robotic leg. As such, advanced robotics including those employing latest artificial intelligence technologies, control system theory and design, and existing biomechanics based controls cannot meet the needs of real time learning control of a powered prosthetic leg in a human-prosthesis system. Given the nature of the problem, reinforcement learning based adaptive optimal control, also referred to as adaptive dynamic programming (ADP), holds great promise to delivering the next generation of prosthesis control solutions.
Intellectual Merit: The design challenge requires innovative approaches of real time reinforcement learning control. The learning controller has to be designed without knowing an explicit dynamic system model describing the human-prosthesis system, while assuring human user safety and system stability, and being scalable and adaptable to different users and use conditions. Putting it all together, the success of this project will be an important milestone for machine learning, control engineering, and rehabilitation engineering.
Broader Impacts: This research has a direct impact on improving the lives of above-knee amputees. Also of great societal impact is the potential of reducing health care cost. New knowledge gained from human-robot interaction will not only aid amputees but also stroke patients who use exoskeleton as assistive devices. The proposed research will also benefit several research communities such as wearable robots, machine learning, and rehabilitation to develop new technologies addressing real applications. To excite and educate future leaders and researchers in science and engineering, the project will provide an opportunity for integration of our research work into graduate education and postdoc training.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
