The Mobile Manipulation Challenge [Competitions

Balakirsky, Stephen

doi:10.1109/mra.2010.938833

Cited by 3 publications

(1 citation statement)

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“…Complete systems that can perform complex and precise manipulation tasks (Bagnell et al, 2012;Hudson et al, 2012;Righetti et al, 2014) are also presented in various robotic challenges (Balakirsky, 2010;Balakirsky et al, 2012;Hackett et al, 2013;Pratt and Manzo, 2013). These systems attempt to explore the problems associated with building complete, intelligent, and flexible manipulation systems.…”

Section: Fine Manipulation For Assemblymentioning

confidence: 99%

Multi-scale assembly with robot teams

Dogar

Knepper

Spielberg

et al. 2015

The International Journal of Robotics Research

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We present algorithms and experiments for multi-scale assembly of complex structures by multi-robot teams. We focus on tasks where successful completion requires multiple types of assembly operations with a range of precision requirements. We develop a hierarchical planning approach to multi-scale perception in support of multi-scale manipulation, in which the resolution of the perception operation is * Corresponding author; e-mail: mdogar@csail.mit.edu 1 The first three authors contributed equally to this paper. This paper is a revision of Dogar et al. (2014) matched with the required resolution for the manipulation operation. We demonstrate these techniques in the context of a multi-step task where robots assemble large box-like objects, inspired by the assembly of an airplane wing. The robots begin by transporting a wing panel, a coarse manipulation operation that requires a wide field of view, and gradually shift to narrower field of view but more accurate sensors for part alignment and fastener insertion. Within this framework we also provide for failure detection and recovery: upon losing track of a feature, the robots retract to using wider field of view systems to re-localize. Finally, we contribute collaborative manipulation algorithms for assembling complex large objects. First, the team of robots coordinates to transport large assembly parts which are too 2 heavy for a single robot to carry. Second, the fasteners and parts are co-localized for robust insertion and fastening. We implement these ideas using four KUKA youBot robots and present experiments where our robots successfully complete all 80 of the attempted fastener insertion operations.

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Section: Fine Manipulation For Assemblymentioning

confidence: 99%

Multi-scale assembly with robot teams

Dogar

Knepper

Spielberg

et al. 2015

The International Journal of Robotics Research

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Learning Novel Objects for Extended Mobile Manipulation

Nagaoka

Sugiura

Nagai

et al. 2011

J Intell Robot Syst

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An Extended Mobile Manipulation Robot Learning Novel Objects

Nagaoka¹,

Attamimi²,

Sugiura³

et al. 2012

JRSJ

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It is convenient for users to teach novel objects to a domestic service robot with a simple procedure. In this paper, we propose a method for learning the images and names of these objects shown by the users. The object images are segmented out from cluttered scenes by using motion attention. Phoneme recognition and voice conversion are used for the speech recognition and synthesis of the object names that are out of vocabulary. In the experiments conducted with 120 everyday objects, we have obtained an accuracy of 91% for object recognition and an accuracy of 82% for word recognition. Furthermore, we have implemented the proposed method on a physical robot, DiGORO, and evaluated its performance by using RoboCup@Home's "Supermarket" task. The results have shown that DiGORO has outperformed the highest score obtained in the RoboCup@Home 2009 competition.

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The Mobile Manipulation Challenge [Competitions

Cited by 3 publications

References 0 publications

Multi-scale assembly with robot teams

Multi-scale assembly with robot teams

Learning Novel Objects for Extended Mobile Manipulation

An Extended Mobile Manipulation Robot Learning Novel Objects

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