English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Grasp planning for multi-fingered hands is still a challenging task due tothe high nonlinear quality metrics, the high dimensionality of hand postureconfiguration, and complex object shapes. Analytical-based grasp planningalgorithms formulate the grasping problem as a constraint optimization problemusing advanced convex optimization solvers. However, these are not guaranteedto find a globally optimal solution. Data-driven based algorithms utilizemachine learning algorithm frameworks to learn the grasp policy using enormoustraining data sets. This paper presents a new approach for grasp generation byformulating a global optimization problem with Bayesian optimization.Furthermore, we parameterize the object shape utilizing the Gaussian ProcessImplicit Surface (GPIS) to integrate the object shape information into theoptimization process. Moreover, a chart defined on the object surface is usedto refine palm pose locally. We introduced a dual optimization stage tooptimize the palm pose and contact points separately. We further extend theBayesian optimization by utilizing the alternating direction method ofmultipliers (ADMM) to eliminate contact optimization constraints. We conductthe experiments in the graspit! Simulator that demonstrates the effectivenessof this approach quantitatively and qualitatively. Our approach achieves a 95%success rate on various commonly objects with diverse shapes, scales, andweights

Grasp Planning for Flexible Production with Small Lot Sizes based on CAD models using GPIS and Bayesian Optimization