This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.
The use of touch sensing as part of a large system being built for 3D shape recovery and object recognition using touch and vision methods is described. The authors focus on three exploratory procedures they have devised to acquire and interpret sparse 3D touch data: grasping by containment, planar surface exploration, and surface contour exploration. Experimental results for each of these procedures are presented. > Abstract Two experiments establish links between desired knowledge about objects and hand movements during haptic object exploration. Experiment 1 used a match-to-sample task, in which blindfolded subjects were directed to match objects on a particular dimension (e.g., texture). Hand movements during object exploration were reliably classified as “exploratory procedures,” each procedure defined by its invariant and typical properties. The movement profile, i.e., the distribution of exploratory procedures, was directly related to the desired object knowledge that was required for the match. Experiment 2 addressed the reasons for the specific links between exploratory procedures and knowledge goals. Hand movements were constrained, and performance on various matching tasks was assessed. The procedures were considered in terms of their necessity, sufficiency, and optimality of performance for each task. The results establish that in free exploration, a procedure is generally used to acquire information about an object property, not because it is merely sufficient, but because it is optimal or even necessary. Hand movements can serve as “windows,” through which it is possible to learn about the underlying representation of objects in memory and the processes by which such representations are derived and utilized.  This paper discusses how data from multiple tactile sensors may be used to identify and locate one object, from among a set of known objects. We use only local information from sensors: 1) the position of contact points and 2) ranges of surface normals at the contact points. The recognition and localization process is structured as the development and pruning of a tree of consistent hypotheses about pairings between contact points and object surfaces. In this paper, we deal with polyhedral objects constrained to lie on a known plane, i.e., having three degrees of positioning freedom relative to the sensors. We illustrate the performance of the algorithm by simulation. This paper discusses how local measurements of three-dimensional positions and surface normals (recorded by a set of tactile sensors, or by three-dimensional range sensors), may be used to identify and locate objects from among a set of known objects. The objects are modeled as polyhedra having up to six degrees of freedom relative to the sensors. We show that inconsistent hypotheses about pairings between sensed points and object surfaces can be discarded efficiently by using local constraints on distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. We show by simulation and by mathematical bounds that the number of hypotheses consistent with these constraints is small We also show how to recover the position and orientation of the object from the sensory data. The algorithm's performance on data obtained from a triangulation range sensor is illustrated. 