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2015


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Untethered Magnetic Micromanipulation

Diller, E., Sitti, M.

In Micro-and Nanomanipulation Tools, 13, 10, Wiley-VCH Verlag GmbH & Co. KGaA, November 2015 (inbook)

Abstract
This chapter discusses the methods and state of the art in microscale manipulation in remote environments using untethered microrobotic devices. It focuses on manipulation at the size scale of tens to hundreds of microns, where small size leads to a dominance of microscale physical effects and challenges in fabrication and actuation. To motivate the challenges of operating at this size scale, the chapter includes coverage of the physical forces relevant to microrobot motion and manipulation below the millimeter-size scale. It then introduces the actuation methods commonly used in untethered manipulation schemes, with particular focus on magnetic actuation due to its wide use in the field. The chapter divides these manipulation techniques into two types: contact manipulation, which relies on direct pushing or grasping of objects for motion, and noncontact manipulation, which relies indirectly on induced fluid flow from the microrobot motion to move objects without any direct contact.

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DOI Project Page [BibTex]

2015


DOI Project Page [BibTex]


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Lernende Roboter

Trimpe, S.

In Jahrbuch der Max-Planck-Gesellschaft, Max Planck Society, May 2015, (popular science article in German) (inbook)

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link (url) [BibTex]

link (url) [BibTex]


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Autonomous Robots

Schaal, S.

In Jahrbuch der Max-Planck-Gesellschaft, May 2015 (incollection)

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[BibTex]

[BibTex]


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Robot Learning

Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J. A., Schaal, S.

In Springer Handbook of Robotics 2nd Edition, pages: 1371-1394, Springer Berlin Heidelberg, Berlin, Heidelberg, 2015 (incollection)

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[BibTex]

[BibTex]

2010


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Nanohandling robot cells

Fatikow, Sergej, Wich, Thomas, Dahmen, Christian, Jasper, Daniel, Stolle, Christian, Eichhorn, Volkmar, Hagemann, Saskia, Weigel-Jech, Michael

In Handbook of Nanophysics: Nanomedicine and Nanorobotics, pages: 1-31, CRC Press, 2010 (incollection)

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[BibTex]

2010


[BibTex]


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Locally weighted regression for control

Ting, J., Vijayakumar, S., Schaal, S.

In Encyclopedia of Machine Learning, pages: 613-624, (Editors: Sammut, C.;Webb, G. I.), Springer, 2010, clmc (inbook)

Abstract
This is article addresses two topics: learning control and locally weighted regression.

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link (url) [BibTex]

link (url) [BibTex]


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Atomic-Force-Microscopy-Based Nanomanipulation Systems

Onal, C. D., Ozcan, O., Sitti, M.

In Handbook of Nanophysics: Nanomedicine and Nanorobotics, pages: 1-15, CRC Press, 2010 (incollection)

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[BibTex]

[BibTex]

2007


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Dynamics systems vs. optimal control ? a unifying view

Schaal, S, Mohajerian, P., Ijspeert, A.

In Progress in Brain Research, (165):425-445, 2007, clmc (inbook)

Abstract
In the past, computational motor control has been approached from at least two major frameworks: the dynamic systems approach and the viewpoint of optimal control. The dynamic system approach emphasizes motor control as a process of self-organization between an animal and its environment. Nonlinear differential equations that can model entrainment and synchronization behavior are among the most favorable tools of dynamic systems modelers. In contrast, optimal control approaches view motor control as the evolutionary or development result of a nervous system that tries to optimize rather general organizational principles, e.g., energy consumption or accurate task achievement. Optimal control theory is usually employed to develop appropriate theories. Interestingly, there is rather little interaction between dynamic systems and optimal control modelers as the two approaches follow rather different philosophies and are often viewed as diametrically opposing. In this paper, we develop a computational approach to motor control that offers a unifying modeling framework for both dynamic systems and optimal control approaches. In discussions of several behavioral experiments and some theoretical and robotics studies, we demonstrate how our computational ideas allow both the representation of self-organizing processes and the optimization of movement based on reward criteria. Our modeling framework is rather simple and general, and opens opportunities to revisit many previous modeling results from this novel unifying view.

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link (url) [BibTex]

2007


link (url) [BibTex]


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Bacteria integrated swimming microrobots

Behkam, B., Sitti, M.

In 50 years of artificial intelligence, pages: 154-163, Springer Berlin Heidelberg, 2007 (incollection)

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[BibTex]

[BibTex]