One-shot learning and generation of dexterous grasps for novel objects

By Marek Kopicki, Renaud Detry, Maxime Adjigble, Rustam Stolkin, Ales Leonardis, Jeremy L. Wyatt. 2016

139 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Control and Systems Engineering, Human-Computer Interaction

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Citations in the corpus

Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2016, methodology): “Data-driven methods take a variety of different forms, including human-supervised methods that predict grasp configurations (Ben Amor et al., 2012; Herzog et al., 2014; Kopicki et al., 2016; Lenz et al., 2015) and methods that predict finger placement from geometric criteria computed offline (Goldfe”
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration (2017, methodology): “In recent years, LfD was successfully used in the field of robotics for applications as varied as autonomous helicopter maneuvers [1], playing table tennis [2], object manipulation [3], making coffee [4], grasping novel objects [5], carrot grating [6], and eggplant cutting [7].”
Deep learning a grasp function for grasping under gripper pose uncertainty (2016, background): “For determining object grasp poses from images, use of hand-engineered features still performs well in complex cases such as dense clutter [30] or multi-fingered grasping [17].”
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network (2019, background): “Early research in this field involved hand-engineering the features [5], [6], which can be a tedious and time-consuming task but can be helpful for learning to grasp objects with multiple fingers such as [7], [8].”
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, background): “Interactions may be modeled at this level of the hierarchy as contact points [192, 75, 209, 408, 364, 403, 298].”
Active vision for dexterous grasping of novel objects (2016, background): “Grasp planning with current Γt based on Kopicki, Wyatt et al [12].”
High precision grasp pose detection in dense clutter (2016, background): “optimizes over the combination of a contact and a hand configuration model to generate grasp candidates [9].”