From caging to grasping

By Alberto Rodriguez, Matthew Thomas Mason, Steve Ferry. 2011

284 citations recorded by the source corpus.

This paper digs into the relationship between cages and grasps of a rigid body. In particular, it considers the use of cages as waypoints to grasp an object. We introduce the concept of pregrasping cages, caging configurations from which an object can be grasped without first breaking the cage. For two-fingered manipulators, all cages are pregrasping cages and, consequently, useful waypoints to grasp an object. A contribution of this paper is to show that the same does not hold for more than two fingers. A second contribution is to show how to overcome that limitation. We explore the natural generalization of the well-understood squeezing/stretching characterization of two-finger cages to arbitrary workspace dimension, arbitrary object shapes without holes, and arbitrary number of point fingers, and exploit it to give sufficient conditions for a cage to be a pregrasping cage. As a product of that generalization, we introduce grasping functions: scalar functions defined on the finger formation that control the process of going from a cage to a grasp. We finish the paper by establishing an analogy between the role of grasping functions in grasping and that of Lyapunov functions in stability theory.

Subfields: Atomic and Molecular Physics, and Optics, Control and Systems Engineering

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Citations in the corpus

Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning (2018, background): “Grasping too, has been well studied in the domain of model-based reasoning [19]; from modeling contact forces and their resistance to external wrenches [20], [21], to characterizing grasps by their ability to constrain object mobility [22].”
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, methodology): “Geometric methods analyze the shape of a target object and plan a suitable grasp pose, based on criteria such as force closure (Weisz and Allen, 2012) or caging (Rodriguez et al., 2012).”
Deep learning for detecting robotic grasps (2013, neutral): “have refined these definitions [50].”
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018, methodology): “Geometric methods employ analytic grasp metrics, such as force closure [9] or caging [10].”
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics (2017, methodology): “An alternative approach is to plan grasps using physicsbased analytic metrics based on caging [50], grasp wrench space (GWS) analysis [48], or robust GWS analysis, which can be rapidly computed using Cloud Computing [27].”
Data-Driven Grasp Synthesis—A Survey (2013, methodology): “Although methods like this relax some of the assumptions, they are still limited to simulation [14, 10] or consider 2D objects [14, 15, 16].”
Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks (2018, background): “Traditionally, grasping was usually solved analytically, where 3D meshes of objects would be used to compute the stability of a grasp against external wrenches [45, 47] or constrain the object’s motion [47].”
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods (2018, background): “Prior work in robotic manipulation has sought to address the grasping problem through a wide range of methods, from analytic grasp metrics [43], [36] to learning-based approaches [2].”
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch (2018, background): “A significant body of work in robotics has studied analytic grasping models, which use known or estimated models of object geometry, environments, and robot grippers, and which typically make use of manually defined grasping metrics [4]–[6].”
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation (2017, unknown): “Both geometry-based [13], [14], [15] and data-driven methods [16] have been proposed to address the problem of robotic grasping.”
Domain Randomization and Generative Models for Robotic Grasping (2017, background): “wrenches [38] or constrain the object’s motion [39].”
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes? (2017, unknown): “A significant body of work in robotics has studied analytic grasping models, which use models of object geometry, environments, and robot grippers, and which typically make use of a manually defined grasping metric [12, 13, 14].”
Learning Visual Affordances for Robotic Manipulation (2019, background): “Grasping too, has been well studied in the domain of model-based reasoning; from modeling contact forces and their resistance to external wrenches [91, 116], to characterizing grasps by their ability to constrain object mobility [96].”
Learning task-oriented grasping for tool manipulation from simulated self-supervision (2018, background): “They optimize grasp quality measures based on analytic models of constraints deriving from geometry and physics, such as force closure and form closure (Ferrari and Canny, 1992; Rodriguez et al., 2012; Weisz and Allen, 2012).”
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands (2022, methodology): “Although analytic metrics have been successfully applied to parallel-jaw gripper grasp synthesis (based on grasp wrench space analysis [27, 35, 63], robust grasp wrench space analysis [60, 86], or caging [62, 74]), more recent works [19, 24, 48, 66] have focused on simulation-based synthesis.”
ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact (2019, background): “Analytic approaches [13], [14], [15], [16], [17], [18], [19], [20] synthesize grasp(s) from object shape, which usually guarantee stability within their set of assumptions.”