Using Experience for Assessing Grasp Reliability

By Antonio Morales, Eris Chinellato, Andrew H. Fagg, Angel Pascual del Pobil. 2004

80 citations recorded by the source corpus.

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

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Citations in the corpus

The Curious Robot: Learning Visual Representations via Physical Interactions (2016, background): “Most initial work focused on using human annotators [21] but in this work we are more interested in building a self-supervision system [22,23,24,25].”
Data-Driven Grasp Synthesis—A Survey (2013, background): “Speth et al. [92] showed that their earlier 2D based approach [95] is also applicable when considering 3D objects.”
One-shot learning and generation of dexterous grasps for novel objects (2016, methodology): “Finally, we finish with an experimental study (Section 5) and a discussion (Section 6).”
A data-driven statistical framework for post-grasp manipulation (2014, methodology): “[15] used real grasps on a collection of objects to predict the reliability of the grasp process.”
Learning motion primitive goals for robust manipulation (2011, background): “Uncertainty in an object’s position can make even the most carefully planned motion fail, as the object might simply not be at the location where the planner expects it to be [10], [1].”
Learning to grasp under uncertainty (2011, background): “In our empirical evaluation, we demonstrate how the motion primitives become more robust when grasping simple cylindrical objects, as well as more complex, non-convex objects.”
Domain Randomization and Generative Models for Robotic Grasping (2017, background): “As a result, significant research attention has been given to data-driven grasp synthesis methods [2], [8], [27]–[29],”
Learning Visual Affordances for Robotic Manipulation (2019, methodology): “This is inspired by recent data-driven methods for grasp planning [77, 102, 59, 93, 86, 87, 66, 37, 61], which do not rely on object identities or state estimation.”
Leveraging big data for grasp planning (2015, background): “We have already mentioned relevant work in the introduction that uses differently acquired datasets to learn grasp mappings from vision based representations to grasp success using classifiers trained in a supervised manner [15] [16] [21] [22] [26] [28] [31] [35] [36].”