OpenGRASP: A Toolkit for Robot Grasping Simulation

By Beatriz León, Stefan Ulbrich, Rosen Diankov, G. Puche, Markus Przybylski, Antonio Morales, Tamim Asfour, S. Moisio, Jeannette Bohg, James J. Kuffner Jr.. 2010

145 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering, Mechanical Engineering

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Citations in the corpus

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “ instance segmentation were output from the model directly. Robot grasping is inherently a detection problem. Traditionally it was solved through matching with full 3-D model (Miller and Allen, 2004; Leon et al., 2010; Pelossof et al.,´ 2004), where a strong prior information is necessary. Jiang et ”
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics (2017, methodology): “As wrench space analysis can be computationally expensive, a common method is to precompute a database of known 3D objects labeled with grasps and the quality of each, such as GraspIt! [16] or OpenGRASP [32].”
Real-time grasp detection using convolutional neural networks (2014, background): “Significant past work uses 3-D simulations to find good grasps [3] [4] [5] [6] [7].”
The YCB object and Model set: Towards common benchmarks for manipulation research (2015, methodology): “The suite is based on the OpenGRASP toolkit [20] which supplies an interface to import objects and robot models as well as plugins for actuators and tactile sensors.”
Learning of grasp selection based on shape-templates (2013, background): “(Miller and Allen 2004), Open-Rave (Diankov and Kuffner 2008) or OpenGRASP (Leon et al. 2010; Ulbrich et al. 2011) that can execute grasps given perfect knowledge of the object and its environment.”
Learning Grasping Interaction with Geometry-aware 3D Representations (2017, background): “Some work focused on analytic modeling of robotic grasps with known object shape information Goldfeder et al. [2009], Leon et al. [2010].”
VisGraB: A benchmark for vision-based grasping (2012, methodology): “Our benchmark methodology is based on open xml formats and can hence be used with other grasp simulators, such as GraspIt [23] and OpenGRASP [22].”
UniSim: A Neural Closed-Loop Sensor Simulator (2023, background): “Several works then developed accurate physics engines for improving robot design and motion planning [7,13,15,28,33], and for specific domains such as grasping [36], soft robotics [27], and SDVs [88].”
AllenAct: A Framework for Embodied AI Research (2020, methodology): “Platforms such as RLBench [5], Sapien [6], and Meta-World [7] focus on manipulation tasks, while [46, 47] enable studying the task of grasping.”