  Human is able to conduct 3D recognition by a limited number of haptic
contacts between the target object and his/her fingers without seeing the
object. This capability is defined as `haptic glance' in cognitive
neuroscience. Most of the existing 3D recognition models were developed based
on dense 3D data. Nonetheless, in many real-life use cases, where robots are
used to collect 3D data by haptic exploration, only a limited number of 3D
points could be collected. In this study, we thus focus on solving the
intractable problem of how to obtain cognitively representative 3D key-points
of a target object with limited interactions between the robot and the object.
A novel reinforcement learning based framework is proposed, where the haptic
exploration procedure (the agent iteratively predicts the next position for the
robot to explore) is optimized simultaneously with the objective 3D recognition
with actively collected 3D points. As the model is rewarded only when the 3D
object is accurately recognized, it is driven to find the sparse yet efficient
haptic-perceptual 3D representation of the object. Experimental results show
that our proposed model outperforms the state of the art models.
