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 ...

The fine-structure energy levels in carbon atom are calculated with the extended average level multi-configurational Dirac-Fock method in the generalpurpose relativistic atomic structure package (GRASP). Dirac Atomic R-matrix Code was used to obtain the lowenergy excitation cross sections of the P1 and P2 thresholds for the edge plasma conditions.

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...

We focus on grasp planning for a humanoid multi-fingered hand attached at the tip of a humanoid robot’s arm. The hand has potential possibility to grasp various objects under several situations. Since the multi-fingered hand can use several grasp types such as fingertip grasp, and envelope grasp with taking the advantage of degrees of freedom. We develop grasp planner which selects a feasible g...

Machine vision-based planar grasping detection is challenging due to uncertainty about object shape, pose, size, etc. Previous methods mostly focus on predicting discrete gripper configurations, and may miss some ground-truth grasp postures. In this article, a pixel-level method proposed, which uses deep neural network predict configurations RGB images. First, novel oriented arrow representatio...