  Robotic tactile sensing provides a method of recognizing objects and their
properties where vision fails. Prior work on tactile perception in robotic
manipulation has frequently focused on exploratory procedures (EPs). However,
the also-human-inspired technique of in-hand-manipulation can glean rich data
in a fraction of the time of EPs. We propose a simple 3-DOF robotic hand
design, optimized for object rolling tasks via a variable-width palm and
associated control system. This system dynamically adjusts the distance between
the finger bases in response to object behavior. Compared to fixed finger
bases, this technique significantly increases the area of the object that is
exposed to finger-mounted tactile arrays during a single rolling motion (an
increase of over 60% was observed for a cylinder with a 30-millimeter
diameter). In addition, this paper presents a feature extraction algorithm for
the collected spatiotemporal dataset, which focuses on object corner
identification, analysis, and compact representation. This technique
drastically reduces the dimensionality of each data sample from 10 x 1500 time
series data to 80 features, which was further reduced by Principal Component
Analysis (PCA) to 22 components. An ensemble subspace k-nearest neighbors (KNN)
classification model was trained with 90 observations on rolling three
different geometric objects, resulting in a three-fold cross-validation
accuracy of 95.6% for object shape recognition.
