This is the first year funding of a three year continuing award. This research seeks to understand shape-aware robotic manipulation in order to improve the versatility of robots interacting with their environments. As robots become more human-assistive, they will need to manipulate a large variety of novel objects of varying shapes and sizes. Key to recognizing and manipulating these objects will be the integration of numerous passive and active sensory modalities. This research focuses on the information content of active tactile manipulation strategies. For instance, when grasping an object of known shape and dynamics but unknown pose, the time history of the contact points on the fingers often provides enough constraint to reveal the orientation of the object. More difficult is the task of picking up an object of unknown shape and unknown dynamics. Again, the finger configurations required to touch the object provide constraints on the possible shapes of the object. As the fingers move and the object slips in the fingers, these constraints evolve over time, thereby permitting reconstruction of the shape of the object. The difference between theses tasks lies primarily in the number of fingers required to touch the object, the degree of active manipulation, and the character of internal models. In between these extremes of known and unknown shape lie more typical tasks, in which partial shape information exists. For instance, a human or robot may know the rough shape and pose of an object from visual inspection; grasping strategies then reveal the fine shape and dynamics. The impact of this research lies most directly in manipulation, with possible additional impact on haptic displays and industrial automation. The shape-sensing routines produced by this research will make it easier to program robots. Rather than requiring a robot programmer to measure object shapes and locations precisely, this research will provide automatic methods for extracting that information during robot manipulation. Such automatic methods will facilitate robotic aids. Moreover, understanding the nature of the shape information produced during manipulation will facilitate the design of better haptic devices.
