  Imitation learning for robot dexterous manipulation, especially with a real
robot setup, typically requires a large number of demonstrations. In this
paper, we present a data-efficient learning from demonstration framework which
exploits the use of rich tactile sensing data and achieves fine bimanual pinch
grasping. Specifically, we employ a convolutional autoencoder network that can
effectively extract and encode high-dimensional tactile information. Further,
We develop a framework that achieves efficient multi-sensor fusion for
imitation learning, allowing the robot to learn contact-aware sensorimotor
skills from demonstrations. Our comparision study against the framework without
using encoded tactile features highlighted the effectiveness of incorporating
rich contact information, which enabled dexterous bimanual grasping with active
contact searching. Extensive experiments demonstrated the robustness of the
fine pinch grasp policy directly learned from few-shot demonstration, including
grasping of the same object with different initial poses, generalizing to ten
unseen new objects, robust and firm grasping against external pushes, as well
as contact-aware and reactive re-grasping in case of dropping objects under
very large perturbations. Furthermore, the saliency map analysis method is used
to describe weight distribution across various modalities during pinch
grasping, confirming the effectiveness of our framework at leveraging
multimodal information.
