Robotic Prosthetics Learn From Their Error’s


New research coming out of Europe may change all that, ushering in a new generation of BMI systems.

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In a study published today in Nature Scientific Reports, researcher Jose Millán of the Center for Neuroprosthetics at EPFL unveiled a new kind of artificially intelligent BMI system that learns from its mistakes.

It works like this: Existing neuroprostheses require the user to generate specific brainwave activities for particular motions — “extend left arm,” for instance. If the triggering brain activity isn’t precisely correct, the desired action fails and the brain emits an electrical signal signifying the failure.

Millán’s team has found a way to make use of those error signals by teaching the machine itself to learn from mistakes. When the neuroprosthetic system detects the error message from the brain, it understands that the action was unsuccessful and adjusts movements accordingly.

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If a patient is trying to grasp an object, for example, the intelligent prosthesis will make adjustments and increase precision on its own until no error messages are generated and the goal is achieved.

“The paradigm shift lies in the use of these signals to relieve the subject from the tedious task of learning,” according to the press materials issued by EPFL.


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