Push-manipulation of complex passive mobile objects using experimentally acquired motion models

By Tekin Meriçli, Manuela M. Veloso, H. L. Akin. 2014

64 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning (2018, background): “While recent methods have explored data-driven algorithms for learning the dynamics of pushing [17], [18], [19], many of these works have largely focused on the execution of stable pushes for one object at a time.”
Learning to Poke by Poking: Experiential Learning of Intuitive Physics (2016, methodology): “In robotic manipulation, a number of prior methods have been proposed that use hand-designed visual features and known object poses or key locations to plan and execute pushes and other non-prehensile manipulations (Kopicki et al., 2011; Lau et al., 2011; Meriçli et al., 2015).”
Learning Visual Affordances for Robotic Manipulation (2019, background): “While recent methods have explored data-driven algorithms for learning the dynamics of pushing [100, 73, 128], many of these works have largely focused on the execution of stable”
Learning task-oriented grasping for tool manipulation from simulated self-supervision (2018, background): “Sweeping, on the other hand, is a primitive in autonomous manipulation, such as in part positioning and reorientation [33], grasping in clutter [9], object manipulation without lifting [39].”
A probabilistic data-driven model for planar pushing (2017, background): “Over the years, several works have applied data-driven techniques to the problem of planar pushing [12, 13, 14, 15, 16].”
Policy transfer via modularity (2017, background): “Some previous efforts have been made to deal with the uncertainties in friction by learning how to push objects [19], [20], [21], [22], [23], [18].”
Policy transfer via modularity and reward guiding (2017, background): “Some previous efforts have been made to deal with the uncertainties in friction by learning how to push objects [24], [25], [26], [27], [28], [20], [29].”
Model Identification via Physics Engines for Improved Policy Search (2017, methodology): “A nonparametric approach was employed for learning the outcome of pushing large objects [18].”