VisGraB: A benchmark for vision-based grasping
By Gert Kootstra, Mila Popovic, Jimmy Alison Jørgensen, Danica Kragic, Henrik Gordon Petersen, Norbert Krüger. 2012
33 citations recorded by the source corpus.
We present a database and a software tool, VisGraB, for benchmarking of methods for vision-based grasping of unknown objects with no prior object knowledge. The benchmark is a combined real-world and simulated experimental setup. Stereo images of real scenes containing several objects in different configurations are included in the database. The user needs to provide a method for grasp generation based on the real visual input. The grasps are then planned, executed, and evaluated by the provided grasp simulator where several grasp-quality measures are used for evaluation. This setup has the advantage that a large number of grasps can be executed and evaluated while dealing with dynamics and the noise and uncertainty present in the real world images. VisGraB enables a fair comparison among different grasping methods. The user furthermore does not need to deal with robot hardware, focusing on the vision methods instead. As a baseline, benchmark results of our grasp strategy are included.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Pieter Abbeel
- Siddhartha S. Srinivasa
- Aaron Walsman
- Berk Çalli
- Aaron M. Dollar
- Arjun Singh
- Jeannette Bohg
- Danica Kragic
In Computer Vision and Pattern Recognition
- Roozbeh Mottaghi
- Aniruddha Kembhavi
- Jitendra Malik
- Luc Van Gool
- Trevor Darrell
- Kristen Grauman
- Li Fei-Fei
- Cordelia Schmid
In Control and Systems Engineering
- Sergey Levine
- Pieter Abbeel
- Jürgen Leitner
- Jeannette Bohg
- Danica Kragic
- Siddhartha S. Srinivasa
- Chelsea Finn
- Roozbeh Mottaghi
Citations in the corpus
- Data-Driven Grasp Synthesis—A Survey (2013, methodology): “the Columbia Grasp database [140], the VisGraB data set [141] or the playpen data set [142] they are not commonly used for comparison.”
- The YCB object and Model set: Towards common benchmarks for manipulation research (2015, background): “The VisGraB [21] provides a benchmark framework for grasping unknown objects.”
- Benchmarking Simulated Robotic Manipulation Through a Real World Dataset (2019, background): “and those available have been developed to assess the performance of planners and grasping [14]–[16].”
- 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.”
- Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols (2015, background): “6 VisGraB [21] 2012 Stereo images Manipulation 18 No http://www.”
- Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set (2015, background): “6 VisGraB [22] 2012 Stereo images Manipulation 18 No http://www.”