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Thursday, October 15, 2015

Reading Your Mind

This "Psychic Robot" Can Read Your Mind

Researchers have created an algorithm that understands what movement you meant to make, even if you're interrupted.

Emily Matchar | October 14, 2015

Researchers at the University of Illinois at Chicago have created a “psychic robot.” The robot is based on an algorithm that can understand the intention behind a movement—you intended to turn the steering wheel, you intended to take a step, you intended to push the red button—even when that movement is interrupted. 


<more at; related links: (‘Psychic robot’ will know what you really meant to do. October 6, 2015) and (I Meant to Do That: Determining the Intentions of Action in the Face of Disturbances. Justin Horowitz and James Patton. Published: September 1, 2015DOI: 10.1371/journal.pone.0137289. [Abstract: Our actions often do not match our intentions when there are external disturbances such as turbulence. We derived a novel modeling approach for determining this motor intent from targeted reaching motions that are disturbed by an unexpected force. First, we demonstrated how to mathematically invert both feedforward (predictive) and feedback controls to obtain an intended trajectory. We next examined the model’s sensitivity to a realistic range of parameter uncertainties, and found that the expected inaccuracy due to all possible parameter mis-estimations was less than typical movement-to-movement variations seen when humans reach to similar targets. The largest sensitivity arose mainly from uncertainty in joint stiffnesses. Humans cannot change their intent until they acquire sensory feedback, therefore we tested the hypothesis that a straight-line intent should be evident for at least the first 120 milliseconds following the onset of a disturbance. As expected, the intended trajectory showed no change from undisturbed reaching for more than 150 milliseconds after the disturbance onset. Beyond this point, however, we detected a change in intent in five out of eight subjects, surprisingly even when the hand is already near the target. Knowing such an intent signal is broadly applicable: enhanced human-machine interaction, the study of impaired intent in neural disorders, the real-time determination (and manipulation) of error in training, and complex systems that embody planning such as brain machine interfaces, team sports, crowds, or swarms. In addition, observing intent as it changes might act as a window into the mechanisms of planning, correction, and learning.]>

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