Data-Driven Grasp Synthesis - A Survey <s> B. Final Notes <s> 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. <s> BIB001 </s> Data-Driven Grasp Synthesis - A Survey <s> B. Final Notes <s> In this work, we present a new software environment for the comparative evaluation of algorithms for grasping and dexterous manipulation. The key aspect in its development is to provide a tool that allows the reproduction of well-defined experiments in real-life scenarios in every laboratory and, hence, benchmarks that pave the way for objective comparison and competition in the field of grasping. In order to achieve this, experiments are performed on a sound open-source software platform with an extendable structure in order to be able to include a wider range of benchmarks defined by robotics researchers. The environment is integrated into the OpenGRASP toolkit that is built upon the OpenRAVE project and includes grasp-specific extensions and a tool for the creation/integration of new robot models. Currently, benchmarks for grasp and motion planningare included as case studies, as well as a library of domestic everyday objects models, and a real-life scenario that features a humanoid robot acting in a kitchen. <s> BIB002
In this survey, we reviewed work on data-driven grasp synthesis and propose a categorization of the published work. We focus on the type and level of prior knowledge used in the proposed approaches. We looked at what assumptions are commonly made about the objects being manipulated as well as about the complexity of scenes in which grasping is demonstrated and evaluated. We identified recent trends in the field and provided a discussion about the remaining challenges. An important issue is the current lack of general benchmarks and performance metrics suitable for comparing the different approaches. Although various object-grasp databases are already available e.g. the Columbia Grasp database BIB001 , the VisGraB data set  or the playpen data set  they are not commonly used for comparison. We acknowledge that one of the reasons is that grasping in itself is highly dependent on the employed sensing and manipulation hardware. There have also been robotic challenges organized such as the DARPA Arm project  or RoboCup@Home  and a framework for benchmarking has been proposed by Ulbrich et al. BIB002 . However, none of these successfully integrate all the subproblems relevant for grasping. Given that data-driven grasp synthesis is an active field of research and lots of work has been reported in the area, we set up a web page that contains all the references in this survey at www.robotic-grasping.com. They are structured according to the proposed classification and tagged with the mentioned aspect. The web page will be constantly updated with the most recent approaches.