When manipulating parts, it is important to determine the orientation of the part with respect to thegripper. This orientation may not be known precisely or may be disturbed by the act of grasping. It isin some cases possible to use mechanical compliance to orient parts during grasping. It was recentlyshown [14] that any part with polygonal boundary can be oriented and grasped i...

With the goal of advancing the state of automatic robotic grasping, we present a novel approach that combines machine learning techniques and physical validation on a robotic platform to develop a comprehensive grasp predictor. After collecting a large grasp sample set (522 grasps), we first conduct a statistical analysis of the predictive ability of grasp quality metrics that are commonly used...

Path-relinking is a major enhancement to the basic greedy randomized adaptive search procedure (GRASP), leading to significant improvements in solution time and quality. Path-relinking adds a memory mechanism to GRASP by providing an intensification strategy that explores trajectories connecting GRASP solutions and the best elite solutions previously produced during the search. This paper revie...

We distinguish between two types of grasp stability, which we call spatial grasp stability and contact grasp stability. We show via examples that spatial stability cannot capture certain intuitive concepts of grasp stability and hence that any full understanding of grasp stability must include contact stability. We derive a model of how the positions of the points of contact evolve in time on t...

Our goal is to automate the understanding of natural hand-object manipulation by developing computer visionbased techniques. Our hypothesis is that it is necessary to model the grasp types of hands and the attributes of manipulated objects in order to accurately recognize manipulation actions. Specifically, we focus on recognizing hand grasp types, object attributes and actions from a single im...

This paper describes libcgrpp, a GNU-style dynamic shared Python/C library of the continuous greedy randomized adaptive search procedure (C-GRASP) for bound constrained global optimization. C-GRASP is an extension of the GRASP metaheuristic (Feo and Resende, 1989). After a brief introduction to C-GRASP, we show how to download, install, configure, and use the library through an illustrative exa...

Understanding how the CNS controls reach-to-grasp will require behavioral and neurophysiological studies of reach-to-grasp in the monkey, including the evaluation of whole-hand grasp with explicit force requirements. In this study, monkeys performed a reach-to-grasp task in which the size, shape, and orientation of the objects were varied. The monkeys were required to grasp each object at five ...

Objects are often movable in the environment and do not have to be grasped from the presented placement. •Pre-grasp interaction can adjust object configuration in the environment to improve the task conditions for the final grasp. •Our video observation surveys the variety of pre-grasp interactions used by people in natural task settings. •The observed pre-grasp interactions can be described by...

Continuous GRASP (C-GRASP) is a stochastic local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints (Hirsch et al., 2006). Like a greedy randomized adaptive search procedure (GRASP), a C-GRASP is a multi-start procedure where a starting solution for local improvement is constructed in a greedy randomized fashion. In th...