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Abstracting perception and manipulation in end-user robot programming using Sikuli

Authors: Michael Kasper; Nikolaus Correll; Tom Yeh;

Abstracting perception and manipulation in end-user robot programming using Sikuli

Abstract

We propose a programming paradigm for robotics that has the potential to drastically facilitate robotic programming. Building up on Sikuli, a GUI automation language, we abstract specific robotic perception and control capabilities into first-class objects that are embedded in a simple scripting language. Currently, robotics programming requires a deep understanding of perception, controls and algorithms, knowledge of a specific robot's perception capabilities and kinematics, and finally a substantial amount of software engineering. Although learn-by-demonstration allows also relatively unskilled users to adapt a robot to their needs, this approach is intrinsically limited by the complexity such a program can reach. This paper presents a proof-of-concept for migrating Sikuli from the virtual GUI workspace of computer software to the physical 3D workspace of robotics. It then presents an example use case that illustrates the power of this new approach using a simple script that arranges a set of randomly aligned blocks into a tower using a Baxter robot equipped with an Asus Xtion Pro.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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