Constructing Force- Closure Grasps

By V.-D. Nguyen. 1988

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Citations in the corpus

Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours (2015, background): “Early attempts in the ﬁeld focused on using analytical methods and 3D reasoning for predicting grasp locations and conﬁgurations [1]–[4].”
The Curious Robot: Learning Visual Representations via Physical Interactions (2016, background): “ comprehensive literature review of this area can be found in [15,16]. Most work in this area has focused on using analytical methods and 3D reasoning for predicting grasp locations and congurations [17,18,19,20] but assume the availability of complete physical knowledge of the objects. However, rec”
Data-Driven Grasp Synthesis—A Survey (2013, background): “One line of research follows the approach of independent contact regions (ICRs) as deﬁned by Nguyen [11]: a set of regions on the object in which each ﬁnger can be independently placed anywhere without the grasp loosing the force-closure property.”
Learning to push by grasping: Using multiple tasks for effective learning (2016, background): “for predicting grasp locations and configurations [1], [2], [10], [11].”
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias (2018, background): “Classical approaches focused on physics-based analysis of stability [33] and usually require explicit 3D models of the objects.”
Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey (2018, background): “The former consists in applying kinematic constraints on an object such that the object cannot perform any relative motion (Nguyen 1988), and the latter considers a set of contact points such that contact forces can balance an arbitrary external wrench (Bicchi 1995).”
Domain Randomization and Generative Models for Robotic Grasping (2017, methodology): “The earliest robotic grasping methods used analytical knowledge of a scene to compute an optimal grasp for an object [1], [30], [31], [37], [38], [44].”
CASSL: Curriculum Accelerated Self-Supervised Learning (2017, background): “Classical foundational approaches focus on physicsbased analysis of stability [21].”
PointNetGPD: Detecting Grasp Configurations from Point Sets (2018, background): “Force-closure [13], [20] and GWS analysis are two mainstream grasp quality metrics.”
Robotic grasping and contact: a review (2000, unknown): “Other literature on the synthesis of force{closure grasps include [60, 63, 69, 70].”
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping (2020, methodology): “In contrast, we do not pre-compute labels for the test set, but directly evaluate them by calculating the quality score using force closure metric [29].”
Hands for dexterous manipulation and robust grasping: a difficult road toward simplicity (2000, background): “by [124], [48], [31], [122], while literature on the synthesis of force-closure grasps include [124], [133], [139], [140].”
An overview of 3D object grasp synthesis algorithms (2012, background): “There is a wide disparity in the terminology used in the grasping literature regarding equilibrium, stability, force closure and form closure terms [6, 42, 31, 47, 50, 68].”
Pose error robust grasping from contact wrench space metrics (2012, background): “The independent contact region approach (ICR) introduced in [3] considers the effect of contact location uncertainty on the ε GW S of a grasp.”
Synthesis of force-closure grasps on 3-D objects based on the Q distance (2003, methodology): “Nyugen [10] proposed a geometric method for computing maximal independent twofinger grasps on polygons.”