Topic
GRASP
About: GRASP is a research topic. Over the lifetime, 5457 publications have been published within this topic receiving 112708 citations.
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TL;DR: The different types of world elements and the general robot definition are discussed and the robot library is presented, and the grip analysis and visualization method were presented.
Abstract: A robotic grasping simulator, called Graspit!, is presented as versatile tool for the grasping community. The focus of the grasp analysis has been on force-closure grasps, which are useful for pick-and-place type tasks. This work discusses the different types of world elements and the general robot definition, and presented the robot library. The paper also describes the user interface of Graspit! and present the collision detection and contact determination system. The grasp analysis and visualization method were also presented that allow a user to evaluate a grasp and compute optimal grasping forces. A brief overview of the dynamic simulation system was provided.
1,042 citations
Book•
01 Oct 2004
TL;DR: Building on two widely acclaimed previous editions, Craig Larman has updated this book to fully reflect the new UML 2 standard, to help you master the art of object design, and to promote high-impact, iterative, and skillful agile modeling practices.
Abstract: “This edition contains Larman's usual accurate and thoughtful writing. It is a very good book made even better.” -Alistair Cockburn, author, Writing Effective Use Cases and Surviving OO Projects “Too few people have a knack for explaining things. Fewer still have a handle on software analysis and design. Craig Larman has both.” -John Vlissides, author, Design Patterns and Pattern Hatching “People often ask me which is the best book to introduce them to the world of OO design. Ever since I came across it Applying UML and Patterns has been my unreserved choice.” -Martin Fowler, author, UML Distilled and Refactoring “This book makes learning UML enjoyable and pragmatic by incrementally introducing it as an intuitive language for specifying the artifacts of object analysis and design. It is a well written introduction to UML and object methods by an expert practitioner.” -Cris Kobryn, Chair of the UML Revision Task Force and UML 2.0 Working Group A brand new edition of the world's most admired introduction to object-oriented analysis and design with UML Fully updated for UML 2 and the latest iterative/agile practices Includes an all-new case study illustrating many of the book's key pointsApplying UML and Patterns is the world's #1 business and college introduction to “thinking in objects”-and using that insight in real-world object-oriented analysis and design. Building on two widely acclaimed previous editions, Craig Larman has updated this book to fully reflect the new UML 2 standard, to help you master the art of object design, and to promote high-impact, iterative, and skillful agile modeling practices.Developers and students will learn object-oriented analysis and design (OOA/D) through three iterations of two cohesive, start-to-finish case studies. These case studies incrementally introduce key skills, essential OO principles and patterns, UML notation, and best practices. You won't just learn UML diagrams-you'll learn how to apply UML in the context of OO software development.Drawing on his unsurpassed experience as a mentor and consultant, Larman helps you understand evolutionary requirements and use cases, domain object modeling, responsibility-driven design, essential OO design, layered architectures, “Gang of Four” design patterns, GRASP, iterative methods, an agile approach to the Unified Process (UP), and much more. This edition's extensive improvements include A stronger focus on helping you master OOA/D through case studies that demonstrate key OO principles and patterns, while also applying the UML New coverage of UML 2, Agile Modeling, Test-Driven Development, and refactoring Many new tips on combining iterative and evolutionary development with OOA/D Updates for easier study, including new learning aids and graphics New college educator teaching resources Guidance on applying the UP in a light, agile spirit, complementary with other iterative methods such as XP and Scrum Techniques for applying the UML to documenting architectures A new chapter on evolutionary requirements, and much moreApplying UML and Patterns, Third Edition, is a lucid and practical introduction to thinking and designing with objects-and creating systems that are well crafted, robust, and maintainable.
1,004 citations
TL;DR: This work considers the problem of grasping novel objects, specifically objects that are being seen for the first time through vision, and presents a learning algorithm that neither requires nor tries to build a 3-d model of the object.
Abstract: We consider the problem of grasping novel objects, specifically objects that are being seen for the first time through vision. Grasping a previously unknown object, one for which a 3-d model is not available, is a challenging problem. Furthermore, even if given a model, one still has to decide where to grasp the object. We present a learning algorithm that neither requires nor tries to build a 3-d model of the object. Given two (or more) images of an object, our algorithm attempts to identify a few points in each image corresponding to good locations at which to grasp the object. This sparse set of points is then triangulated to obtain a 3-d location at which to attempt a grasp. This is in contrast to standard dense stereo, which tries to triangulate every single point in an image (and often fails to return a good 3-d model). Our algorithm for identifying grasp locations from an image is trained by means of supervised learning, using synthetic images for the training set. We demonstrate this approach on two robotic manipulation platforms. Our algorithm successfully grasps a wide variety of objects, such as plates, tape rolls, jugs, cellphones, keys, screwdrivers, staplers, a thick coil of wire, a strangely shaped power horn and others, none of which were seen in the training set. We also apply our method to the task of unloading items from dishwashers.
959 citations
10 Nov 1996
TL;DR: Experimental results obtained from a large number of benchmarks, including many from the field of test pattern generation, indicate that application of the proposed conflict analysis techniques to SAT algorithms can be extremely effective for aLarge number of representative classes of SAT instances.
Abstract: This paper introduces GRASP (Generic seaRch Algorithm for the Satisfiability Problem), an integrated algorithmic framework for SAT that unifies several previously proposed search-pruning techniques and facilitates identification of additional ones. GRASP is premised on the inevitability of conflicts during search and its most distinguishing feature is the augmentation of basic backtracking search with a powerful conflict analysis procedure. Analyzing conflicts to determine their causes enables GRASP to backtrack non-chronologically to earlier levels in the search tree, potentially pruning large portions of the search space. In addition, by "recording" the causes of conflicts, GRASP can recognize and preempt the occurrence of similar conflicts later on in the search. Finally, straightforward bookkeeping of the causality chains leading up to conflicts allows GRASP to identify assignments that are necessary for a solution to be found. Experimental results obtained from a large number of benchmarks, including many from the field of test pattern generation, indicate that application of the proposed conflict analysis techniques to SAT algorithms can be extremely effective for a large number of representative classes of SAT instances.
951 citations
Proceedings Article•
27 Jun 2018TL;DR: QT-Opt as mentioned in this paper is a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters.
Abstract: In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execute the desired grasp, our method enables closed-loop vision-based control, whereby the robot continuously updates its grasp strategy based on the most recent observations to optimize long-horizon grasp success. To that end, we introduce QT-Opt, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real-world grasping that generalizes to 96% grasp success on unseen objects. Aside from attaining a very high success rate, our method exhibits behaviors that are quite distinct from more standard grasping systems: using only RGB vision-based perception from an over-the-shoulder camera, our method automatically learns regrasping strategies, probes objects to find the most effective grasps, learns to reposition objects and perform other non-prehensile pre-grasp manipulations, and responds dynamically to disturbances and perturbations.
884 citations