Abstract
As robots make their way out of factories into human environments, outer space, and beyond, they require the skill to manipulate their environment in multifarious, unforeseeable circumstances. With this regard, pushing is an essential motion primitive that dramatically extends a robot's manipulation repertoire. In this work, we review the robotic pushing literature. While focusing on work concerned with predicting the motion of pushed objects, we also cover relevant applications of pushing for planning and control. Beginning with analytical approaches, under which we also subsume physics engines, we then proceed to discuss work on learning models from data. In doing so, we dedicate a separate section to deep learning approaches which have seen a recent upsurge in the literature. Concluding remarks and further research perspectives are given at the end of the paper.
Highlights
We argue that pushing is an essential motion primitive in a robot’s manipulative repertoire
Related to our work is the survey conducted by Ruggiero et al (2018) which covers the literature on planning and control for non-prehensile dynamic manipulation
They characterized the variability of friction, and evaluated the most common assumptions and simplifications made by previous models of frictional pushing
Summary
We argue that pushing is an essential motion primitive in a robot’s manipulative repertoire. Humans perform skilful manipulation tasks from an early age, and are able to transfer behaviors learned on one object to objects of novel sizes, shapes, and physical properties. The large body of work on robotic pushing has produced many accurate models for predicting the outcome of a push, some analytical, and some data-driven. We focus on work concerned with making predictions of the motion of pushed objects, but we cover relevant applications of pushing for planning and control. Related to our work is the survey conducted by Ruggiero et al (2018) which covers the literature on planning and control for non-prehensile dynamic manipulation. We conclude by summarizing the presented approaches and by discussing open problems and promising directions for future research (section 5)
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