Recognizing Teleoperated Manipulations
نویسندگان
چکیده
The many degrees-of-freedom and distributed sensing capability of dextrous robot hands permits the use of control programs that rely on qualitative changes in sensor feedback rather than precise positioning and force information. One way of designing such a control program is to have the robot learn the qualitative control characteristics from examples. A convenient way of providing these examples is via teleoperation. To this end, this paper presents results for recognizing and segmenting manipulation primitives from a teleop-erated task by analysis of features in sensor feedback. k-nearest quantized pattern vectors determine potential classiications. A hidden Markov model provides task context for the nal segmentation. The illustrative task is picking up a plastic egg with a spatula.
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