Error Fields: Personalized robotic movement training that augments one's more likely mistakes.

Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We have shown that augmenting error can enhance learning, but while such findings are encouraging the methods need to be refined to accommodate a person's individual reactions to error. The current study evaluates error fields (EF) method, where the interactive robot tempers its augmentation when the error is less likely. 22 healthy participants were asked to learn moving with a visual transformation, and we enhanced the training with error fields. We found that training with error fields led to greatest reduction in error. EF training reduced error 264% more than controls who practiced without error fields, but subjects learned more slowly than our previous error magnification technique. We also found a relationship between the amount of learning and how much variability was induced by the error augmentation treatments, most likely leading to better exploration and discovery of the causes of error. These robotic training enhancements should be further explored in combination to optimally leverage error statistics to teach people how to move better.

Transfer of dynamic motor skills acquired during isometric training to free motion.

Recent studies have explored the prospects of learning to move without moving, by displaying virtual arm movement related to exerted force. However, it has yet to be tested whether learning the dynamics of moving can transfer to the corresponding movement. Here we present a series of experiments that investigate this isometric training paradigm. Subjects were asked to hold a handle and generate forces as their arms were constrained to a static position. A precise simulation of reaching was used to make a graphic rendering of an arm moving realistically in response to the measured interaction forces and simulated environmental forces. Such graphic rendering was displayed on a horizontal display that blocked their view to their actual (statically constrained) arm and encouraged them to believe they were moving. We studied adaptation of horizontal, planar, goal-directed arm movements in a velocity-dependent force field. Our results show that individuals can learn to compensate for such a force field in a virtual environment and transfer their new skills to the actual free motion condition, with performance comparable to practice while moving. Such nonmoving techniques should impact various training conditions when moving may not be possible.NEW & NOTEWORTHY This study provided early evidence supporting that training movement skills without moving is possible. In contrast to previous studies, our study involves 1) exploiting cross-modal sensory interactions between vision and proprioception in a motionless setting to teach motor skills that could be transferable to a corresponding physical task, and 2) evaluates the movement skill of controlling muscle-generated forces to execute arm movements in the presence of external forces that were only virtually present during training.

Forces That Supplement Visuomotor Learning: A "Sensory Crossover" Experiment.

Previous studies on reaching movements have shown that people can adapt to either visuomotor (e.g., prism glasses) or mechanical distortions (e.g., force fields) through repetitive practice. Recent work has shown that adaptation to one type of distortion might have implications on learning the other type, suggesting that neural resources are common to both kinematic and kinetic adaptation. This study investigated whether training with a novel force field might benefit the learning of a visual distortion-specifically, when forces were designed to produce aftereffects that aligned with the ideal trajectory for a visual rotation. Participants training with these forces (Force Group) were tested on a visual rotation. After training with this novel field, we found that participants had surprisingly good performance in the visual rotation condition, comparable to a group that trained on the visual rotation directly. A third group tested the rate of learning with intermittent catch trials, where we zeroed the forces and switched to the visual rotation, and found a significantly faster learning rate than the group that trained directly on the visual rotation. Interestingly, these abilities continued to significantly improve one day later, whereas the direct training showed no such effect. All participants were able to generalize what they learned to unpracticed movement directions. We speculate that when forces are used in training, haptic feedback can have a substantial influence on learning a task that heavily relies on visual feedback. Such methods can impact any situation where one might add robotic forces to the training process.