  Inspired by the human ability to perform complex manipulation in the complete
absence of vision (like retrieving an object from a pocket), the robotic
manipulation field is motivated to develop new methods for tactile-based object
interaction. However, tactile sensing presents the challenge of being an active
sensing modality: a touch sensor provides sparse, local data, and must be used
in conjunction with effective exploration strategies in order to collect
information. In this work, we focus on the process of guiding tactile
exploration, and its interplay with task-related decision making. We propose
TANDEM (TActile exploration aNd DEcision Making), an architecture to learn
efficient exploration strategies in conjunction with decision making. Our
approach is based on separate but co-trained modules for exploration and
discrimination. We demonstrate this method on a tactile object recognition
task, where a robot equipped with a touch sensor must explore and identify an
object from a known set based on binary contact signals alone. TANDEM achieves
higher accuracy with fewer actions than alternative methods and is also shown
to be more robust to sensor noise.
