Grasping unknown objects using an Early Cognitive Vision system for general scene understanding

By Mila Popovic, Gert Kootstra, Jimmy Alison Jørgensen, Danica Kragic, Norbert Krüger. 2011

71 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

RLBench: The Robot Learning Benchmark & Learning Environment (2019, methodology): “For grasping, both OpenGrasp [36] and VisGraB [37] are popular simulation-based benchmarks, whilst the YCB dataset [38] focuses on real-world objects.”
Data-Driven Grasp Synthesis—A Survey (2013, methodology): “27a) Generation of grasp candidates from local surface features and evaluation in simulation [111].”
VisGraB: A benchmark for vision-based grasping (2012, background): “Other Xhand Xhand q0 q1 q2 q3 q4 q5 q6 q2 C1 C2 C3 q1X q2 q3 q4 q5 q6 q7 c-i.a. b. d. e. c-ii. c-iii User’s vision method Figure 1.”
The YCB object and Model set: Towards common benchmarks for manipulation research (2015, background): “Second, in the majority of these datasets, the models are without high-quality texture information (with BigBIRD [1] and KIT [12] datasets standing out as notable exceptions) which is necessary for grasp synthesis algorithms which use feature-based methods (e.g. [16, 17]).”
Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014, methodology): “…Grasp dataset [67], the MIT KIT object dataset [88], and the Willow Garage Household Objects Database [40] are available online and have been used to evaluate different aspects of grasping algorithms, including grasp stability [45], [46], robust grasping [162], and scene understanding [128].”
Leveraging big data for grasp planning (2015, background): “Only a few of those datasets are publicly available [21, 26, 31, 35].”
The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research (2016, background): “[8], [9] OpenGrasp [1] YCB [11] Amazon Picking ACRV Picking [10], [12] VisGraB [13] Challenge [6] Benchmark (ours)”
Benchmarking Simulated Robotic Manipulation Through a Real World Dataset (2019, background): “Few simulated benchmarks exist for manipulation, and those available have been developed to assess the performance of planners and grasping [14], [15], [16].”
A Survey of Research on Cloud Robotics and Automation (2015, background): “New approaches are required that are robust to dirty data.”