The intuitive process of walking is difficult to imitate in a natural, comfortable way for people who use robotic prostheses and exoskeletons. Toward this end, to better teach a prosthetic knee to act as naturally as possible, research collaboration based at Arizona State University (ASU) and North Carolina State University proposed a seemingly obvious instructor: the user’s intact knee. But enabling a robotic prosthesis to work with a human user is a complex problem that cannot be solved with classical control systems. Instead of mathematically describing the individual components and then controlling their actions, Jennie Si, a professor in the ASU School of Electrical, Computer and Energy Engineering said her team observed how the players worked together and then exerted control based on the new knowledge.
Si and her team developed a reinforcement learning algorithm with artificial intelligence to help the robotic knee copy the intact knee and tested it through computer simulations. They constrained the robotic knee’s range to keep the partnered movements relatively stable but gave it the freedom to adjust based on the intact knee’s actions. When walking on even ground at an even pace, the robotic knee mimicked the intact knee 100% of the time. When the terrain became uneven, the success rate dipped to 97%, with an average of just below 20 steps accurately taken. In the third scenario, where the walking pace increased and then slowed, the robotic knee could follow the intact knee with 80% accuracy but improved with more training.
“This is a dynamic problem, meaning the variables evolve over time,” Si said. “The prosthetic can work in symmetry with the human to create a comfortable experience. The user doesn’t need to stop and say, ‘I am going up stairs now,’ and wait for the robot to figure out how to do that. They can just go up the stairs together.”