Collecting grasp data for learning and benchmarking purposes is very expensive. It would be helpful to have a standard database of graspable objects, along with a set of stable grasps for each object, but no such database exists. In this work we show how to automate the construction of a database consisting of several hands, thousands of objects, and hundreds of thousands of grasps. Using this database, we demonstrate a novel grasp planning algorithm that exploits geometric similarity between a 3D model and the objects in the database to synthesize form closure grasps. Our contributions are this algorithm, and the database itself, which we are releasing to the community as a tool for both grasp planning and benchmarking.

To grasp a novel object, we can index it into a database of known 3D models and use precomputed grasp data for those models to suggest a new grasp. We refer to this idea as data-driven grasping, and we have previously introduced the Columbia Grasp Database for this purpose. In this paper we demonstrate a data-driven grasp planner that requires only partial 3D data of an object in order to grasp it. To achieve this, we introduce a new shape descriptor for partial 3D range data, along with an alignment method that can rigidly register partial 3D models to models that are globally similar but not identical. Our method uses SIFT features of depth images, and encapsulates ¿nearby¿ views of an object in a compact shape descriptor.

Text search on databases of 3D models has traditionally worked poorly, as text annotations on 3D models are often unreliable or incomplete. We attempt to improve the recall of text search by automatically assigning appropriate tags to models. Our algorithm finds relevant tags by appealing to a large corpus of partially labeled example models, which does not have to be preclassified or otherwise prepared. For this purpose we use a copy of Google 3D Warehouse, a database of user contributed models which is publicly available on the Internet. Given a model to tag, we find geometrically similar models in the corpus, based on distances in a reduced dimensional space derived from Zernike descriptors. The labels of these neighbors are used as tag candidates for the model with probabilities proportional to the degree of geometric similarity. We show experimentally that text based search for 3D models using our computed tags can approach the quality of geometry based search.

Training a 3D model classifier on a small dataset is very challenging. However, large datasets of partially classified models are now commonly available online. We use an external training set of models with associated text tags to automatically assign tags to both training and query models. The similarity between these tags, used in conjunction with a standard shape descriptor, yields a multiclassifier that outperforms the standalone shape descriptor.

Planning realizable and stable grasps on 3D objects is crucial for many robotics applications, but grasp planners often ignore the relative sizes of the robotic hand and the object being grasped or do not account for physical joint and positioning limitations. We present a grasp planner that can consider the full range of parameters of a real hand and an arbitrary object, including physical and material properties as well as environmental obstacles and forces, and produce an output grasp that can be immediately executed. We do this by decomposing a 3D model into a superquadric 'decomposition tree' which we use to prune the intractably large space of possible grasps into a subspace that is likely to contain many good grasps. This subspace can be sampled and evaluated in GraspIt!, our 3D grasping simulator, to find a set of highly stable grasps, all of which are physically realizable. We show grasp results on various models using a Barrett hand.

In this paper, we build upon recent advances in neuroscience research which have shown that control of the human hand during grasping is dominated by movement in a configuration space of highly reduced dimensionality. We extend this concept to robotic hands and show how a similar dimensionality reduction can be defined for a number of different hand models. This framework can be used to derive planning algorithms that produce stable grasps even for highly complex hand designs. Furthermore, it offers a unified approach for controlling different hands, even if the kinematic structures of the models are significantly different. We illustrate these concepts by building a comprehensive grasp planner that can be used on a large variety of robotic hands under various constraints.

Multiprocessor embedded systems often have processor-local caches and a shared memory. If the system's code is available at design time we can maximize cache hits by rearranging code in memory so that frequently executed tasks reside in reserved areas of the caches and are not overwritten by less frequent tasks.