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2012


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Methods, apparatuses, and systems for micromanipulation with adhesive fibrillar structures

Sitti, M., Mengüç, Y.

December 2012, US Patent App. 14/368,079 (misc)

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[BibTex]

2012



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Dry adhesive structures

Sitti, M., Murphy, M., Aksak, B.

December 2012, US Patent App. 13/533,386 (misc)

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[BibTex]

[BibTex]


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Methods of making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

June 2012, US Patent 8,206,631 (misc)

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[BibTex]

[BibTex]


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Dry adhesives and methods for making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

March 2012, US Patent App. 13/429,621 (misc)

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[BibTex]

[BibTex]


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From Dynamic Movement Primitives to Associative Skill Memories

Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J., Theodorou, E., Schaal, S.

Robotics and Autonomous Systems, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Two-dimensional autonomous microparticle manipulation strategies for magnetic microrobots in fluidic environments

Pawashe, C., Floyd, S., Diller, E., Sitti, M.

IEEE Transactions on Robotics, 28(2):467-477, IEEE, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Three-dimensional microfiber devices that mimic physiological environments to probe cell mechanics and signaling

Ruder, W. C., Pratt, E. D., Bakhru, S., Sitti, M., Zappe, S., Cheng, C., Antaki, J. F., LeDuc, P. R.

Lab on a Chip, 12(10):1775-1779, Royal Society of Chemistry, 2012 (article)

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[BibTex]

[BibTex]


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Active visual search in unknown environments using uncertain semantics

Aydemir, Alper, Pronobis, Andrzej, Jensfelt, Patric, Sj, Kristoffer, Aydemir, Alper, Jensfelt, Patric, Aydemir, A, Jensfelt, P, Aydemir, A, Jensfelt, P, others

Transactions, 1, pages: 2329-2335, IEEE, 2012 (article)

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[BibTex]

[BibTex]


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Modelling of conductive atomic force microscope probes for scanning tunnelling microscope operation

Ozcan, O, Sitti, M

IET Micro \& Nano Letters, 7(4):329-333, IET, 2012 (article)

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[BibTex]

[BibTex]


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Shape memory polymer-based flexure stiffness control in a miniature flapping-wing robot

Hines, L., Arabagi, V., Sitti, M.

IEEE Transactions on Robotics, 28(4):987-990, IEEE, 2012 (article)

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[BibTex]

[BibTex]


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Micro-manipulation using rotational fluid flows induced by remote magnetic micro-manipulators

Ye, Z., Diller, E., Sitti, M.

Journal of Applied Physics, 112(6):064912, AIP, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Remotely addressable magnetic composite micropumps

Diller, E., Miyashita, S., Sitti, M.

Rsc Advances, 2(9):3850-3856, Royal Society of Chemistry, 2012 (article)

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[BibTex]

[BibTex]


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Model-free reinforcement learning of impedance control in stochastic environments

Stulp, Freek, Buchli, Jonas, Ellmer, Alice, Mistry, Michael, Theodorou, Evangelos A., Schaal, S.

Autonomous Mental Development, IEEE Transactions on, 4(4):330-341, 2012 (article)

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[BibTex]

[BibTex]


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Shape-Programmable Soft Capsule Robots for Semi-Implantable Drug Delivery

Yim, S., Sitti, M.

Mechatronics, IEEE/ASME Transactions on, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Control of multiple heterogeneous magnetic microrobots in two dimensions on nonspecialized surfaces

Diller, E., Floyd, S., Pawashe, C., Sitti, M.

IEEE Transactions on Robotics, 28(1):172-182, IEEE, 2012 (article)

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[BibTex]

[BibTex]


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Gecko-Inspired Controllable Adhesive Structures Applied to Micromanipulation

Mengüç, Y., Yang, S. Y., Kim, S., Rogers, J. A., Sitti, M.

Advanced Functional Materials, 22(6):1245-1245, WILEY-VCH Verlag, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Elastomer surfaces with directionally dependent adhesion strength and their use in transfer printing with continuous roll-to-roll applications

Yang, S. Y., Carlson, A., Cheng, H., Yu, Q., Ahmed, N., Wu, J., Kim, S., Sitti, M., Ferreira, P. M., Huang, Y., others,

Advanced Materials, 24(16):2117-2122, WILEY-VCH Verlag, 2012 (article)

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[BibTex]

[BibTex]


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Effect of retraction speed on adhesion of elastomer fibrillar structures

Abusomwan, U., Sitti, M.

Applied Physics Letters, 101(21):211907, AIP, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Reinforcement Learning with Sequences of Motion Primitives for Robust Manipulation

Stulp, F., Theodorou, E., Schaal, S.

IEEE Transactions on Robotics, 2012 (article)

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[BibTex]

[BibTex]


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Impact and Surface Tension in Water: a Study of Landing Bodies

Shih, B., Laham, L., Lee, K. J., Krasnoff, N., Diller, E., Sitti, M.

Bio-inspired Robotics Final Project, Carnegie Mellon University, 2012 (article)

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[BibTex]

[BibTex]


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Design and rolling locomotion of a magnetically actuated soft capsule endoscope

Yim, S., Sitti, M.

IEEE Transactions on Robotics, 28(1):183-194, IEEE, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Design and manufacturing of a controllable miniature flapping wing robotic platform

Arabagi, V., Hines, L., Sitti, M.

The International Journal of Robotics Research, 31(6):785-800, SAGE Publications Sage UK: London, England, 2012 (article)

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[BibTex]

[BibTex]


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Chemotactic steering of bacteria propelled microbeads

Kim, D., Liu, A., Diller, E., Sitti, M.

Biomedical microdevices, 14(6):1009-1017, Springer US, 2012 (article)

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Project Page [BibTex]

Project Page [BibTex]

2005


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Adhesive microstructure and method of forming same

Fearing, R. S., Sitti, M.

March 2005, US Patent 6,872,439 (misc)

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[BibTex]

2005


[BibTex]


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Composite adaptive control with locally weighted statistical learning

Nakanishi, J., Farrell, J. A., Schaal, S.

Neural Networks, 18(1):71-90, January 2005, clmc (article)

Abstract
This paper introduces a provably stable learning adaptive control framework with statistical learning. The proposed algorithm employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the proposed learning adaptive control algorithm uses both the tracking error and the estimation error to update the parameters. We first discuss statistical learning of nonlinear functions, and motivate our choice of the locally weighted learning framework. Second, we begin with a class of first order SISO systems for theoretical development of our learning adaptive control framework, and present a stability proof including a parameter projection method that is needed to avoid potential singularities during adaptation. Then, we generalize our adaptive controller to higher order SISO systems, and discuss further extension to MIMO problems. Finally, we evaluate our theoretical control framework in numerical simulations to illustrate the effectiveness of the proposed learning adaptive controller for rapid convergence and high accuracy of control.

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link (url) [BibTex]

link (url) [BibTex]


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A model of smooth pursuit based on learning of the target dynamics using only retinal signals

Shibata, T., Tabata, H., Schaal, S., Kawato, M.

Neural Networks, 18, pages: 213-225, 2005, clmc (article)

Abstract
While the predictive nature of the primate smooth pursuit system has been evident through several behavioural and neurophysiological experiments, few models have attempted to explain these results comprehensively. The model we propose in this paper in line with previous models employing optimal control theory; however, we hypothesize two new issues: (1) the medical superior temporal (MST) area in the cerebral cortex implements a recurrent neural network (RNN) in order to predict the current or future target velocity, and (2) a forward model of the target motion is acquired by on-line learning. We use stimulation studies to demonstrate how our new model supports these hypotheses.

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link (url) [BibTex]

link (url) [BibTex]


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Parametric and Non-Parametric approaches for nonlinear tracking of moving objects

Hidaka, Y, Theodorou, E.

Technical Report-2005-1, 2005, clmc (article)

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PDF [BibTex]

PDF [BibTex]

2003


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Synthetic gecko foot-hair micro/nano-structures as dry adhesives

Sitti, M., Fearing, R. S.

Journal of adhesion science and technology, 17(8):1055-1073, Taylor & Francis Group, 2003 (article)

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Project Page [BibTex]

2003


Project Page [BibTex]


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Teleoperated touch feedback from the surfaces at the nanoscale: modeling and experiments

Sitti, M., Hashimoto, H.

IEEE/ASME transactions on mechatronics, 8(2):287-298, IEEE, 2003 (article)

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[BibTex]

[BibTex]


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Scaled teleoperation system for nano-scale interaction and manipulation

Sitti, M., Aruk, B., Shintani, H., Hashimoto, H.

Advanced Robotics, 17(3):275-291, Taylor & Francis Group, 2003 (article)

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[BibTex]

[BibTex]


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Atomic force microscope probe based controlled pushing for nano-tribological characterization

Sitti, M.

IEEE/ASME Transactions on Mechatronics, 8(3), 2003 (article)

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[BibTex]


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Computational approaches to motor learning by imitation

Schaal, S., Ijspeert, A., Billard, A.

Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 358(1431):537-547, 2003, clmc (article)

Abstract
Movement imitation requires a complex set of mechanisms that map an observed movement of a teacher onto one's own movement apparatus. Relevant problems include movement recognition, pose estimation, pose tracking, body correspondence, coordinate transformation from external to egocentric space, matching of observed against previously learned movement, resolution of redundant degrees-of-freedom that are unconstrained by the observation, suitable movement representations for imitation, modularization of motor control, etc. All of these topics by themselves are active research problems in computational and neurobiological sciences, such that their combination into a complete imitation system remains a daunting undertaking - indeed, one could argue that we need to understand the complete perception-action loop. As a strategy to untangle the complexity of imitation, this paper will examine imitation purely from a computational point of view, i.e. we will review statistical and mathematical approaches that have been suggested for tackling parts of the imitation problem, and discuss their merits, disadvantages and underlying principles. Given the focus on action recognition of other contributions in this special issue, this paper will primarily emphasize the motor side of imitation, assuming that a perceptual system has already identified important features of a demonstrated movement and created their corresponding spatial information. Based on the formalization of motor control in terms of control policies and their associated performance criteria, useful taxonomies of imitation learning can be generated that clarify different approaches and future research directions.

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link (url) [BibTex]

link (url) [BibTex]


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Efficient charge recovery method for driving piezoelectric actuators with quasi-square waves

Campolo, D., Sitti, M., Fearing, R. S.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 50(3):237-244, IEEE, 2003 (article)

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[BibTex]

[BibTex]


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Piezoelectrically actuated four-bar mechanism with two flexible links for micromechanical flying insect thorax

Sitti, M.

IEEE/ASME transactions on mechatronics, 8(1):26-36, IEEE, 2003 (article)

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[BibTex]

1997


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Locally weighted learning

Atkeson, C. G., Moore, A. W., Schaal, S.

Artificial Intelligence Review, 11(1-5):11-73, 1997, clmc (article)

Abstract
This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control. Keywords: locally weighted regression, LOESS, LWR, lazy learning, memory-based learning, least commitment learning, distance functions, smoothing parameters, weighting functions, global tuning, local tuning, interference.

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link (url) [BibTex]

1997


link (url) [BibTex]


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Locally weighted learning for control

Atkeson, C. G., Moore, A. W., Schaal, S.

Artificial Intelligence Review, 11(1-5):75-113, 1997, clmc (article)

Abstract
Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control. Keywords: locally weighted regression, LOESS, LWR, lazy learning, memory-based learning, least commitment learning, forward models, inverse models, linear quadratic regulation (LQR), shifting setpoint algorithm, dynamic programming.

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link (url) [BibTex]

link (url) [BibTex]