Push planning for object placement on cluttered table surfaces

By Akansel Cosgun, Tucker Hermans, Victor Emeli, Mike Stilman. 2011

133 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Deep visual foresight for planning robot motion (2016, methodology): “This approach has been applied to a range of problems, including robotic pushing [7], [8], [9].”
GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images (2017, background): “…standard meth-ods in robotics and autonomous vehicle control typically separate perception and decision making into separate stages [20, 34, 8, 7], the success of deep learning meth-ods has prompted considerable recent interest in learning sensorimotor policies that directly map image pixels…”
6-DOF Grasping for Target-driven Object Manipulation in Clutter (2019, background): “exist that tackle scenarios of grasping in structured clutter ([7], [31], [35]–[37]).”
Policy transfer via modularity (2017, background): “Pushing can also be used to move multiple objects at once, such as when clearing space on a cluttered table, in which the robot can sweep multiple objects out of the way in a single stroke [2].”
Policy transfer via modularity and reward guiding (2017, background): “Pushing can also be used to move multiple objects at once, such as when clearing space on a cluttered table, in which the robot can sweep multiple objects out of the way in a single stroke [2].”
Visual Room Rearrangement (2021, methodology): “While these methods have shown impressive performance, they consider complete observability of the state from perfect visual perception [11, 27], a planar surface as the environment [30, 42], a static robot [15, 32], same environment for evaluation of generalization [39, 26], or a limited set of…”
Rearrangement: A Challenge for Embodied AI (2020, background): “In the task and motion planning literature, there has been a rich body of work on ‘rearrangement planning’ [6, 16, 18, 21, 41, 42, 45, 63, 64, 66], the problem of searching for a sequence of actions that transform an initial conﬁgu-ration of objects into some goal conﬁguration.”
Learning to Singulate Objects using a Push Proposal Network (2017, background): “3 2 Related Work Standard model-based approaches for object singulation require knowledge of object properties to perform motion planning in a physics simulator and to choose push actions accordingly [6, 7]. However, estimating the location of objects and other properties of the physical environment”