  Tactile perception is important for robotic systems that interact with the
world through touch. Touch is an active sense in which tactile measurements
depend on the contact properties of an interaction--e.g., velocity, force,
acceleration--as well as properties of the sensor and object under test. These
dependencies make training tactile perceptual models challenging. Additionally,
the effects of limited sensor life and the near-field nature of tactile sensors
preclude the practical collection of exhaustive data sets even for fairly
simple objects. Active learning provides a mechanism for focusing on only the
most informative aspects of an object during data collection. Here we employ an
active learning approach that uses a data-driven model's entropy as an
uncertainty measure and explore relative to that entropy conditioned on the
sensor state variables. Using a coverage-based ergodic controller, we train
perceptual models in near-real time. We demonstrate our approach using a
biomimentic sensor, exploring "tactile scenes" composed of shapes, textures,
and objects. Each learned representation provides a perceptual sensor model for
a particular tactile scene. Models trained on actively collected data
outperform their randomly collected counterparts in real-time training tests.
Additionally, we find that the resulting network entropy maps can be used to
identify high salience portions of a tactile scene.
