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2012


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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]

2012


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]

2010


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Policy learning algorithmis for motor learning (Algorithmen zum automatischen Erlernen von Motorfähigkigkeiten)

Peters, J., Kober, J., Schaal, S.

Automatisierungstechnik, 58(12):688-694, 2010, clmc (article)

Abstract
Robot learning methods which allow au- tonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to ful- fill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general ap- proach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human- like performance. For doing so, we study two major components for such an approach, i. e., firstly, we study policy learning algo- rithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structu- res for task representation and execution.

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link (url) [BibTex]


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Gait planning based on kinematics for a quadruped gecko model with redundancy

Son, D., Jeon, D., Nam, W. C., Chang, D., Seo, T., Kim, J.

Robotics and Autonomous Systems, 58, 2010 (article)

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[BibTex]

[BibTex]


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Flat dry elastomer adhesives as attachment materials for climbing robots

Unver, O., Sitti, M.

IEEE transactions on robotics, 26(1):131-141, IEEE, 2010 (article)

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[BibTex]

[BibTex]


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A Bayesian approach to nonlinear parameter identification for rigid-body dynamics

Ting, J., DSouza, A., Schaal, S.

Neural Networks, 2010, clmc (article)

Abstract
For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods.

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link (url) [BibTex]


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A first optimal control solution for a complex, nonlinear, tendon driven neuromuscular finger model

Theodorou, E. A., Todorov, E., Valero-Cuevas, F.

Proceedings of the ASME 2010 Summer Bioengineering Conference August 30-September 2, 2010, Naples, Florida, USA, 2010, clmc (article)

Abstract
In this work we present the first constrained stochastic op- timal feedback controller applied to a fully nonlinear, tendon driven index finger model. Our model also takes into account an extensor mechanism, and muscle force-length and force-velocity properties. We show this feedback controller is robust to noise and perturbations to the dynamics, while successfully handling the nonlinearities and high dimensionality of the system. By ex- tending prior methods, we are able to approximate physiological realism by ensuring positivity of neural commands and tendon tensions at all timesthus can, for the first time, use the optimal control framework to predict biologically plausible tendon tensions for a nonlinear neuromuscular finger model. METHODS 1 Muscle Model The rigid-body triple pendulum finger model with slightly viscous joints is actuated by Hill-type muscle models. Joint torques are generated by the seven muscles of the index fin-

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PDF [BibTex]

PDF [BibTex]


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An experimental analysis of elliptical adhesive contact

Sümer, B., Onal, C. D., Aksak, B., Sitti, M.

Journal of Applied Physics, 107(11):113512, AIP, 2010 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Efficient learning and feature detection in high dimensional regression

Ting, J., D’Souza, A., Vijayakumar, S., Schaal, S.

Neural Computation, 22, pages: 831-886, 2010, clmc (article)

Abstract
We present a novel algorithm for efficient learning and feature selection in high- dimensional regression problems. We arrive at this model through a modification of the standard regression model, enabling us to derive a probabilistic version of the well-known statistical regression technique of backfitting. Using the Expectation- Maximization algorithm, along with variational approximation methods to overcome intractability, we extend our algorithm to include automatic relevance detection of the input features. This Variational Bayesian Least Squares (VBLS) approach retains its simplicity as a linear model, but offers a novel statistically robust â??black- boxâ? approach to generalized linear regression with high-dimensional inputs. It can be easily extended to nonlinear regression and classification problems. In particular, we derive the framework of sparse Bayesian learning, e.g., the Relevance Vector Machine, with VBLS at its core, offering significant computational and robustness advantages for this class of methods. We evaluate our algorithm on synthetic and neurophysiological data sets, as well as on standard regression and classification benchmark data sets, comparing it with other competitive statistical approaches and demonstrating its suitability as a drop-in replacement for other generalized linear regression techniques.

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link (url) [BibTex]

link (url) [BibTex]


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Stochastic Differential Dynamic Programming

Theodorou, E., Tassa, Y., Todorov, E.

In the proceedings of American Control Conference (ACC 2010) , 2010, clmc (article)

Abstract
We present a generalization of the classic Differential Dynamic Programming algorithm. We assume the existence of state- and control-dependent process noise, and proceed to derive the second-order expansion of the cost-to-go. Despite having quartic and cubic terms in the initial expression, we show that these vanish, leaving us with the same quadratic structure as standard DDP.

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PDF [BibTex]

PDF [BibTex]


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Enhanced wet adhesion and shear of elastomeric micro-fiber arrays with mushroom tip geometry and a photopolymerized p (DMA-co-MEA) tip coating

Glass, P., Chung, H., Washburn, N. R., Sitti, M.

Langmuir, 26(22):17357-17362, American Chemical Society, 2010 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Learning control in robotics – trajectory-based opitimal control techniques

Schaal, S., Atkeson, C. G.

Robotics and Automation Magazine, 17(2):20-29, 2010, clmc (article)

Abstract
In a not too distant future, robots will be a natural part of daily life in human society, providing assistance in many areas ranging from clinical applications, education and care giving, to normal household environments [1]. It is hard to imagine that all possible tasks can be preprogrammed in such robots. Robots need to be able to learn, either by themselves or with the help of human supervision. Additionally, wear and tear on robots in daily use needs to be automatically compensated for, which requires a form of continuous self-calibration, another form of learning. Finally, robots need to react to stochastic and dynamic environments, i.e., they need to learn how to optimally adapt to uncertainty and unforeseen changes. Robot learning is going to be a key ingredient for the future of autonomous robots. While robot learning covers a rather large field, from learning to perceive, to plan, to make decisions, etc., we will focus this review on topics of learning control, in particular, as it is concerned with learning control in simulated or actual physical robots. In general, learning control refers to the process of acquiring a control strategy for a particular control system and a particular task by trial and error. Learning control is usually distinguished from adaptive control [2] in that the learning system can have rather general optimization objectivesâ??not just, e.g., minimal tracking errorâ??and is permitted to fail during the process of learning, while adaptive control emphasizes fast convergence without failure. Thus, learning control resembles the way that humans and animals acquire new movement strategies, while adaptive control is a special case of learning control that fulfills stringent performance constraints, e.g., as needed in life-critical systems like airplanes. Learning control has been an active topic of research for at least three decades. However, given the lack of working robots that actually use learning components, more work needs to be done before robot learning will make it beyond the laboratory environment. This article will survey some ongoing and past activities in robot learning to assess where the field stands and where it is going. We will largely focus on nonwheeled robots and less on topics of state estimation, as typically explored in wheeled robots [3]â??6], and we emphasize learning in continuous state-action spaces rather than discrete state-action spaces [7], [8]. We will illustrate the different topics of robot learning with examples from our own research with anthropomorphic and humanoid robots.

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link (url) [BibTex]

link (url) [BibTex]


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Learning, planning, and control for quadruped locomotion over challenging terrain

Kalakrishnan, M., Buchli, J., Pastor, P., Mistry, M., Schaal, S.

International Journal of Robotics Research, 30(2):236-258, 2010, clmc (article)

Abstract
We present a control architecture for fast quadruped locomotion over rough terrain. We approach the problem by decomposing it into many sub-systems, in which we apply state-of-the-art learning, planning, optimization, and control techniques to achieve robust, fast locomotion. Unique features of our control strategy include: (1) a system that learns optimal foothold choices from expert demonstration using terrain templates, (2) a body trajectory optimizer based on the Zero- Moment Point (ZMP) stability criterion, and (3) a floating-base inverse dynamics controller that, in conjunction with force control, allows for robust, compliant locomotion over unperceived obstacles. We evaluate the performance of our controller by testing it on the LittleDog quadruped robot, over a wide variety of rough terrains of varying difficulty levels. The terrain that the robot was tested on includes rocks, logs, steps, barriers, and gaps, with obstacle sizes up to the leg length of the robot. We demonstrate the generalization ability of this controller by presenting results from testing performed by an independent external test team on terrain that has never been shown to us.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Teleoperated 3-D force feedback from the nanoscale with an atomic force microscope

Onal, C. D., Sitti, M.

IEEE Transactions on nanotechnology, 9(1):46-54, IEEE, 2010 (article)

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[BibTex]

[BibTex]


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Roll and pitch motion analysis of a biologically inspired quadruped water runner robot

Park, H. S., Floyd, S., Sitti, M.

The International Journal of Robotics Research, 29(10):1281-1297, SAGE Publications Sage UK: London, England, 2010 (article)

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[BibTex]

[BibTex]


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Microstructured elastomeric surfaces with reversible adhesion and examples of their use in deterministic assembly by transfer printing

Kim, Seok, Wu, Jian, Carlson, Andrew, Jin, Sung Hun, Kovalsky, Anton, Glass, Paul, Liu, Zhuangjian, Ahmed, Numair, Elgan, Steven L, Chen, Weiqiu, others

Proceedings of the National Academy of Sciences, 107(40):17095-17100, National Acad Sciences, 2010 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Tankbot: A palm-size, tank-like climbing robot using soft elastomer adhesive treads

Unver, O., Sitti, M.

The International Journal of Robotics Research, 29(14):1761-1777, SAGE Publications Sage UK: London, England, 2010 (article)

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[BibTex]

[BibTex]


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Note: Aligned deposition and modal characterization of micron and submicron poly (methyl methacyrlate) fiber cantilevers

Nain, A. S., Filiz, S., Burak Ozdoganlar, O., Sitti, M., Amon, C.

Review of Scientific Instruments, 81(1):016102, AIP, 2010 (article)

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[BibTex]

[BibTex]


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Enhanced adhesion of dopamine methacrylamide elastomers via viscoelasticity tuning

Chung, H., Glass, P., Pothen, J. M., Sitti, M., Washburn, N. R.

Biomacromolecules, 12(2):342-347, American Chemical Society, 2010 (article)

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Project Page [BibTex]

Project Page [BibTex]

2006


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An ultrasonic standing-wave-actuated nano-positioning walking robot: piezoelectric-metal composite beam modeling

Son, K. J., Kartik, V., Wickert, J. A., Sitti, M.

Journal of vibration and control, 12(12):1293-1309, Sage Publications, 2006 (article)

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[BibTex]

2006


[BibTex]


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IEEE TRANSACTIONS ON ROBOTICS

VOLZ, RICHARD A, TARN, TJ, MACIEJEWSKI, ANTHONY A, LEE, SUKHAN, BICCHI, ANTONIO, DE LUCA, ALESSANDRO, LUH, PETER B, TAYLOR, RUSSELL H, BEKEY, GEORGE A, ARAI, HIROHIKO, others

2006 (article)

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[BibTex]

[BibTex]


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Design methodology for biomimetic propulsion of miniature swimming robots

Behkam, B., Sitti, M.

Trans.-ASME Journal of Dynamic Systems Measurement and Control, 128(1):36, ASME, 2006 (article)

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Project Page [BibTex]

Project Page [BibTex]


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Augmented reality user interface for an atomic force microscope-based nanorobotic system

Vogl, W., Ma, B. K., Sitti, M.

IEEE transactions on nanotechnology, 5(4):397-406, IEEE, 2006 (article)

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[BibTex]

[BibTex]


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Friction enhancement via micro-patterned wet elastomer adhesives on small intestinal surfaces

Kwon, J., Cheung, E., Park, S., Sitti, M.

Biomedical Materials, 1(4):216, IOP Publishing, 2006 (article)

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[BibTex]

[BibTex]


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Compliant and low-cost humidity nanosensors using nanoporous polymer membranes

Yang, B., Aksak, B., Lin, Q., Sitti, M.

Sensors and Actuators B: Chemical, 114(1):254-262, Elsevier, 2006 (article)

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[BibTex]

[BibTex]


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Task-based and stable telenanomanipulation in a nanoscale virtual environment

Kim, S., Sitti, M.

IEEE Transactions on automation science and engineering, 3(3):240-247, IEEE, 2006 (article)

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[BibTex]

[BibTex]


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Drawing suspended polymer micro-/nanofibers using glass micropipettes

Nain, A. S., Wong, J. C., Amon, C., Sitti, M.

Applied Physics Letters, 89(18):183105, AIP, 2006 (article)

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[BibTex]

[BibTex]


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Biologically inspired polymer microfibers with spatulate tips as repeatable fibrillar adhesives

Kim, S., Sitti, M.

Applied Physics Letters, 89(26):261911-261911, AIP, 2006 (article)

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Project Page [BibTex]


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Two-dimensional vision-based autonomous microparticle manipulation using a nanoprobe

Pawashe, C., Sitti, M.

Journal of Micromechatronics, 3(3):285-306, Brill, 2006 (article)

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[BibTex]

[BibTex]


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A biomimetic climbing robot based on the gecko

Menon, C., Sitti, M.

Journal of Bionic Engineering, 3(3):115-125, 2006 (article)

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[BibTex]

[BibTex]


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Proximal probes based nanorobotic drawing of polymer micro/nanofibers

Nain, A. S., Amon, C., Sitti, M.

IEEE transactions on nanotechnology, 5(5):499-510, IEEE, 2006 (article)

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[BibTex]

[BibTex]

1992


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Ins CAD integrierte Kostenkalkulation (CAD-Integrated Cost Calculation)

Ehrlenspiel, K., Schaal, S.

Konstruktion 44, 12, pages: 407-414, 1992, clmc (article)

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[BibTex]

1992


[BibTex]