Evaluating Pinch Quality of Underactuated Robotic Hands
نویسندگان
چکیده
Underactuated robotic hands are favorable for some applications due to its simplicity in mechanism and control compared with fully actuated hand. Incorporating sensors makes it possible for a hand to accomplish low level task autonomously. This project aims at analyzing pinch quality of an underactuated hand that was developed for underwater teleoperation with 8 sensor channels on each finger, exploring a machine learning method that is able to predict pinch quality reliably for autonomous pinch. The importance of each sensor feature was also evaluated. An average of test error of 4.6% was achieved with feature selection, SVR algorithm and a modified predicting strategy. Keywords—machine learning, underactuated hand, sensor fusion, pinch quality.
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