![]() After about 45 choices, most users have found an assistance profile that they choose repeatedly. She chooses one, and it presents another for comparison. A machine learning algorithm presents her with two assistance profiles that it has selected based on previous data. In both cases, we are creating a model of the user’s preferences and using this model to optimize the user’s experience.” Ellie Wilson, a PhD student in mechanical engineering, demonstrates a new control strategy that customizes assistance provided by a pair of ankle exoskeletons. This is a similar idea, but it’s with exoskeleton assistance settings. ![]() “You give it feedback, a thumbs up or thumbs down, and it curates a radio station based on your feedback. “It’s essentially like Pandora music,” said Elliott Rouse, U-M associate professor of robotics and mechanical engineering and corresponding author of the study in Science Robotics. ![]() This approach enables users to set the exoskeleton assistance based on their preferences using a very simple interface, conducive to implementing on a smartwatch or phone. The user then selects one of these two, and the predictor offers another assistance profile that it believes might be better. Of course, what’s simple for the users is more complex underneath, as a machine learning algorithm repeatedly offers pairs of assistance profiles that are most likely to be comfortable for the wearer. Taking inspiration from music streaming services, a team of engineers at the University of Michigan, Google and Georgia Tech has designed the simplest way for users to program their own exoskeleton assistance settings. Study: User preference optimization for control of ankle exoskeletons using sample efficient active learning (DOI: 10.1126/scirobotics.adg3705) ![]() Image credit: Brenda Ahearn, Michigan Engineering The user chooses one, and it presents another for comparison. Using a simple and convenient touchscreen interface, the algorithm learns the assistance preferences of the wearer Two assistance profiles selected based on previous data. ![]()
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