Grasping

By Gualtiero Fantoni, Marco Santochi. 2019

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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].”
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 physics-based analyses such as caging [46], grasp wrench space (GWS) analysis [44], robust GWS analysis [56], or simulation [25], which can be rapidly computed using Cloud Computing [27].”
Domain Randomization and Generative Models for Robotic Grasping (2017, methodology): “Most approaches use simpliﬁed Coulomb friction and rigid body modeling for computational tractability [30], [37], but some have explored more realistic object and friction models [1], [36], [39].”
Nonprehensile Dynamic Manipulation: A Survey (2018, background): “of grasp restraint: form closure and force closure [2].”
6-DOF Grasping for Target-driven Object Manipulation in Clutter (2019, methodology): “Grasping is a widely studied field in robotics ([1], [2], [12]).”
Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach (2018, methodology): “Analytic methods use mathematical and physical models of geometry, kinematics and dynamics to calculate grasps that are stable [2, 24], but tend to not transfer well to the real world due to the difﬁcultly in modelling physical interactions between a manipulator and an object [2, 26, 27].”
Grasping in the Wild: Learning 6DoF Closed-Loop Grasping From Low-Cost Demonstrations (2019, background): “Classic vision-based grasping solutions often explicitly model contact forces with prior knowledge of object geometry, pose, and dynamics [8]–[11].”