Abstract: A system and method for automatically computing desirable palm grasp configurations of an object by a robotic hand is disclosed. A description of the object's surface is obtained. A grasp configuration of a robotic hand comprising a palm and one or more fingers is selected, which specifies a hand configuration and joint variables of the hand, and a plurality of contact points on the object's surface for the palm and fingers. The coefficient of friction at each of the contact points is determined, and a description of one or more external wrenches to which may apply to the object is acquired. The ability of the robotic hand to hold the object against the external wrenches in the selected configuration is then computed. In some embodiments, a plurality of grasp configurations may be compared to determine which are the most desirable. In other embodiments, the space of hand configurations, or the smaller space of palm contact configurations, may be searched to find the most desirable grasp configurations.

Abstract: A system and method for constructing a 3D scene model comprising 3D objects and representing a scene, based upon a prior 3D scene model. The method comprises the steps of acquiring an image of the scene; initializing the computed 3D scene model to the prior 3D scene model; and modifying the computed 3D scene model to be consistent with the image. The step of modifying the computed 3D scene models consists of the sub-steps of comparing data of the image with objects of the 3D scene model, resulting in associated data and unassociated data; using the unassociated data to compute new objects that are not in the 3D scene model and adding the new objects to the 3D scene model; and using the associated data to detect objects in the prior 3D scene model that are absent and removing the absent objects from the 3D scene model. The present invention may also be used to construct multiple alternative 3D scene models.

Abstract: A method for constructing one or more 3D scene models comprising 3D objects and representing a scene, based upon a prior 3D scene model and a model of scene changes, is described. The method comprises the steps of acquiring an image of the scene; initializing the computed 3D scene model to the prior 3D scene model; and modifying the computed 3D scene model to be consistent with the image, possibly constructing and modifying alternative 3D scene models. In some embodiments, a single 3D scene model is chosen and is the result; in other embodiments, the result is a set of 3D scene models. In some embodiments, a set of possible prior scene models is considered.

Abstract: A system for obtaining a probable 3D map of an occluded surface of an object is provided. The system receives an initial 3D map of a visible surface of the object and identifies one or more symmetries of the initial 3D map. The system computes the probable 3D map of the occluded surface by projecting points of the initial 3D map into occluded regions of space according to the identified symmetries. The system can also comprise an imager for obtaining the initial 3D map. The actual occluded surface cannot be known with absolute certainly because it is occluded; however, the computed 3D map will closely resemble the actual occluded surface in many instances because most objects possess one or more symmetries and the computed 3D map is based on such symmetries that are identified in the initial 3D map of the object.

Abstract: A system and method for recognizing instances of classes in a 2D image using 3D class models and for recognizing instances of objects in a 2D image using 3D class models. The invention provides a system and method for constructing a database of 3D class models comprising a collection of class parts, where each class part includes part appearance and part geometry. The invention also provides a system and method for matching portions of a 2D image to a 3D class model. The method comprises identifying image features in the 2D image; computing an aligning transformation between the class model and the image; and comparing, under the aligning transformation, class parts of the class model with the image features. The comparison uses both the part appearance and the part geometry.

Abstract: A method for rapidly determining feasibility of a force optimization problem and for rapidly solving a feasible force optimization problem is disclosed. The method comprises formulating the force optimization problem or force feasibility problem as a convex optimization problem, formulating a primal barrier subproblem associated with the convex optimization problem, and solving the primal barrier subproblem. The method and related methods may also be used to solve each problem in a set of force optimization problems, determine the minimum or maximum force required to satisfy any of a set of force optimization problems, solve a force closure problem, compute a conservative contact force vector, or solve a feasible force optimization problem with bidirectional forces.

Abstract: A robotic hand comprises a base having fingers and a palm. The fingers define a working volume. The hand is able to grasp objects between the fingers and the palm by varying a distance from the palm to the working volume. Varying this distance can be achieved by either moving the palm towards the working volume or by moving the working volume towards the palm. Robotic hands that move the palm comprise an actuator that extends the palm from the base towards the working volume. Robotic hands that move the working volume relative to the palm have the fingers mounted to a common finger support unit that is configured to translate relative to the base.

Abstract: A system and method for acquiring three-dimensional (3-D) images of a scene. The system includes a projection device for projecting a locally unique pattern (LUP) onto a scene, and sensors for imaging the scene containing the LUP at two or more viewpoints. A computing device matches corresponding pixels in the images by using the local uniqueness of the pattern to produce a disparity map. A range map can then be generated by triangulating points in the imaged scene.