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2011


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

Kalakrishnan, Mrinal, Buchli, Jonas, Pastor, Peter, Mistry, Michael, Schaal, S.

International Journal of Robotics Research, 30(2):236-258, February 2011 (article)

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[BibTex]

2011


[BibTex]


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Collective dynamics of colloids at fluid interfaces

Bleibel, J., Dominguez, A., Oettel, M., Dietrich, S.

European Physical Journal E, 34(11), 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Anomalous transport of a tracer on percolating clusters

Spanner, M., Höfling, F., Schröder-Turk, G. E., Mecke, K., Franosch, T.

Journal of Physics: Condensed Matter, 23(23), 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Dynamics of colloids in confined geometries

Almenar, L., Rauscher, M.

Journal of Physics: Condensed Matter, 23(18), 2011 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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The structure and melting transition of two-dimensional colloidal alloys

Law, A. D., Horozov, T. S., Buzza, D. M. A.

Soft Matter, 7(19):8923-8931, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Second harmonic light scattering from spherical polyelectrolyte brushes

Schürer, B., Hoffmann, M., Wunderlich, S., Harnau, L., Peschel, U., Ballauff, M., Peukert, W.

Journal of Physical Chemistry C, 115, pages: 18302-18309, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Solvation forces in Ising films with long-range boundary fields: density-matrix renormalization-group study

Drzewinski, A., Maciolek, A., Barasinski, A.

Molecular Physics, 109(7-10):1133-1141, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Pulling and pushing a cargo with a catalytically active carrier

Popescu, M. N., Tasinkevych, M., Dietrich, S.

Europhysics Letters, 95(2), 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Critical Casimir forces for Ising films with variable boundary fields

Vasilyev, O., Maciolek, A., Dietrich, S.

Physical Review E, 84(4), 2011 (article)

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DOI [BibTex]


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Study of \pi\pi correlations at LHC and RHIC energies in pp collisions within the quark-gluon string model

Nilsson, M. S., Bravina, L. V., Zabrodin, E. E., Malinina, L. V., Bleibel, J.

Physical Review D, 84(5), 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Micro-rheology on (polymer-grafted) colloids using optical tweezers

Gutsche, C., Elmahdy, M. M., Kegler, K., Semenov, I., Stangner, T., Otto, O., Ueberschär, O., Keyser, U. F., Krüger, M., Rauscher, M., Weeber, R., Harting, J., Kim, Y. W., Lobaskin, V., Netz, R., Kremer, F.

Journal of Physics: Condensed Matter, 23(18), IOP Publishing, Bristol, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Debye-Scherrer rings from block copolymer films with powder-like order

Busch, P., Rauscher, M., Moulin, J.-F., Müller-Buschbaum, P.

Journal of Applied Crystallography, 44(2):370-379, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Aerial righting reflexes in flightless animals

Jusufi, A., Zeng, Y., Full, R., Dudley, R.

Integ. Comp. Biol. , 2011 (article)

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[BibTex]

[BibTex]


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Effective interactions and equilibrium configurations of colloidal particles on a sessile droplet

Guzowski, J., Tasinkevych, M., Dietrich, S.

Soft Matter, 7(9):4189-4197, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Capillary interactions in Pickering emulsions

Guzowski, J., Tasinkevych, M., Dietrich, S.

Physical Review E, 84(3), 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Critical Casimir forces steered by patterned substrates

Gambassi, A., Dietrich, S.

Soft Matter, 7(4):1247-1253, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Interaction strength between proteins and polyelectrolyte brushes: A small angle X-ray scattering study

Henzler, K., Haupt, B., Rosenfeldt, S., Harnau, L., Narayanan, T., Ballauff, M.

Physical Chemistry Chemical Physics, 13, pages: 17599-17605, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Annealing of single lamella nanoparticles of polyethylene

Rochette, C. N., Rosenfeldt, S., Henzler, K., Polzer, F., Ballauff, M., Tong, Q., Mecking, S., Drechsler, M., Narayanan, T., Harnau, L.

Macromolecules, 44(12):4845-4851, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Bayesian robot system identification with input and output noise

Ting, J., D’Souza, A., Schaal, S.

Neural Networks, 24(1):99-108, 2011, 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]

link (url) [BibTex]


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Trapping colloids near chemical stripes via critical Casimir forces

Tröndle, M., Zvyagolskaya, O., Gambassi, A., Vogt, D., Harnau, L., Bechinger, C., Dietrich, S.

Molecular Physics, 109, pages: 1169-1185, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Electrostatic interactions in critical solvents

Bier, M., Gambassi, A., Oettel, M., Dietrich, S.

Europhysics Letters, 95, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Structural investigation of thin diblock copolymer films using time-of-flight grazing-incidence small-angle neutron scattering

Metwalli, E., Moulin, J.-F., Rauscher, M., Kaune, G., Ruderer, M. A., Van Bürck, U., Haese-Seiller, M., Kampmann, R., Müller-Buschbaum, P.

Journal of Applied Crystallography, 44(1):84-92, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Complexation of beta-lactoglobulin fibrils and sulfated polysaccharides

Jones, O. G., Handschin, S., Adamcik, J., Harnau, L., Bolisetty, S., Mezzenga, R.

Biomacromolecules, 12, pages: 3056-3065, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Learning variable impedance control

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

International Journal of Robotics Research, 2011, clmc (article)

Abstract
One of the hallmarks of the performance, versatility, and robustness of biological motor control is the ability to adapt the impedance of the overall biomechanical system to different task requirements and stochastic disturbances. A transfer of this principle to robotics is desirable, for instance to enable robots to work robustly and safely in everyday human environments. It is, however, not trivial to derive variable impedance controllers for practical high degree-of-freedom (DOF) robotic tasks. In this contribution, we accomplish such variable impedance control with the reinforcement learning (RL) algorithm PISq ({f P}olicy {f I}mprovement with {f P}ath {f I}ntegrals). PISq is a model-free, sampling based learning method derived from first principles of stochastic optimal control. The PISq algorithm requires no tuning of algorithmic parameters besides the exploration noise. The designer can thus fully focus on cost function design to specify the task. From the viewpoint of robotics, a particular useful property of PISq is that it can scale to problems of many DOFs, so that reinforcement learning on real robotic systems becomes feasible. We sketch the PISq algorithm and its theoretical properties, and how it is applied to gain scheduling for variable impedance control. We evaluate our approach by presenting results on several simulated and real robots. We consider tasks involving accurate tracking through via-points, and manipulation tasks requiring physical contact with the environment. In these tasks, the optimal strategy requires both tuning of a reference trajectory emph{and} the impedance of the end-effector. The results show that we can use path integral based reinforcement learning not only for planning but also to derive variable gain feedback controllers in realistic scenarios. Thus, the power of variable impedance control is made available to a wide variety of robotic systems and practical applications.

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link (url) [BibTex]

link (url) [BibTex]


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Magnetization of multicomponent ferrofluids

Szalai, I., Dietrich, S.

Journal of Physics: Condensed Matter, 23(32), 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Space-resolved dynamics of a tracer in a disordered solid

Franosch, T., Spanner, M., Bauer, T., Schröder-Turk, G. E., Höfling, F.

Journal of Non-Crystalline Solids, 357(2):472-478, 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Long-wavelength anomalies in the asymptotic behavior of mode-coupling theory

Schnyder, S. K., Höfling, F., Franosch, T., Voigtmann, T.

Journal of Physics: Condensed Matter, 23(23), 2011 (article)

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DOI [BibTex]

DOI [BibTex]


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Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

Degallier, S., Righetti, L., Gay, S., Ijspeert, A.

Autonomous Robots, 31(2-3):155-181, October 2011 (article)

Abstract
Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching).

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Understanding haptics by evolving mechatronic systems

Loeb, G. E., Tsianos, G.A., Fishel, J.A., Wettels, N., Schaal, S.

Progress in Brain Research, 192, pages: 129, 2011 (article)

am

[BibTex]

[BibTex]


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Intelligent Mobility—Autonomous Outdoor Robotics at the DFKI

Joyeux, S., Schwendner, J., Kirchner, F., Babu, A., Grimminger, F., Machowinski, J., Paranhos, P., Gaudig, C.

KI, 25(2):133-139, May 2011 (article)

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DOI [BibTex]

DOI [BibTex]

1998


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Constructive incremental learning from only local information

Schaal, S., Atkeson, C. G.

Neural Computation, 10(8):2047-2084, 1998, clmc (article)

Abstract
We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model as well as the parameters of the locally linear model itself are learned independently, i.e., without the need for competition or any other kind of communication. Independent learning is accomplished by incrementally minimizing a weighted local cross validation error. As a result, we obtain a learning system that can allocate resources as needed while dealing with the bias-variance dilemma in a principled way. The spatial localization of the linear models increases robustness towards negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained in isolation, and as a learning system which profits from combining independent expert knowledge on the same problem. This paper illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields. 

am

link (url) [BibTex]

1998


link (url) [BibTex]


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Local adaptive subspace regression

Vijayakumar, S., Schaal, S.

Neural Processing Letters, 7(3):139-149, 1998, clmc (article)

Abstract
Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic prerequisites for the success of autonomous robot devices as well as biological movement systems. So far, due to sparsity of data in high dimensional spaces, learning in such settings requires a significant amount of prior knowledge about the learning task, usually provided by a human expert. In this paper we suggest a partial revision of the view. Based on empirical studies, we observed that, despite being globally high dimensional and sparse, data distributions from physical movement systems are locally low dimensional and dense. Under this assumption, we derive a learning algorithm, Locally Adaptive Subspace Regression, that exploits this property by combining a dynamically growing local dimensionality reduction technique  as a preprocessing step with a nonparametric learning technique, locally weighted regression, that also learns the region of validity of the regression. The usefulness of the algorithm and the validity of its assumptions are illustrated for a synthetic data set, and for data of the inverse dynamics of human arm movements and an actual 7 degree-of-freedom anthropomorphic robot arm. 

am

link (url) [BibTex]

link (url) [BibTex]

1995


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Memory-based neural networks for robot learning

Atkeson, C. G., Schaal, S.

Neurocomputing, 9, pages: 1-27, 1995, clmc (article)

Abstract
This paper explores a memory-based approach to robot learning, using memory-based neural networks to learn models of the task to be performed. Steinbuch and Taylor presented neural network designs to explicitly store training data and do nearest neighbor lookup in the early 1960s. In this paper their nearest neighbor network is augmented with a local model network, which fits a local model to a set of nearest neighbors. This network design is equivalent to a statistical approach known as locally weighted regression, in which a local model is formed to answer each query, using a weighted regression in which nearby points (similar experiences) are weighted more than distant points (less relevant experiences). We illustrate this approach by describing how it has been used to enable a robot to learn a difficult juggling task. Keywords: memory-based, robot learning, locally weighted regression, nearest neighbor, local models.

am

link (url) [BibTex]

1995


link (url) [BibTex]

1994


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Robot juggling: An implementation of memory-based learning

Schaal, S., Atkeson, C. G.

Control Systems Magazine, 14(1):57-71, 1994, clmc (article)

Abstract
This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local modeling approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its prediction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements during exploration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real-time learning of the task within 40 to 100 trials.

am

link (url) [BibTex]

1994


link (url) [BibTex]

1993


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Design concurrent calculation: A CAD- and data-integrated approach

Schaal, S., Ehrlenspiel, K.

Journal of Engineering Design, 4, pages: 71-85, 1993, clmc (article)

Abstract
Besides functional regards, product design demands increasingly more for further reaching considerations. Quality alone cannot suffice anymore to compete in the market; design for manufacturability, for assembly, for recycling, etc., are well-known keywords. Those can largely be reduced to the necessity of design for costs. This paper focuses on a CAD-based approach to design concurrent calculation. It will discuss how, in the meantime well-established, tools like feature technology, knowledge-based systems, and relational databases can be blended into one coherent concept to achieve an entirely CAD- and data-integrated cost information tool. This system is able to extract data from the CAD-system, combine it with data about the company specific manufacturing environment, and subsequently autonomously evaluate manufacturability aspects and costs of the given CAD-model. Within minutes the designer gets quantitative in-formation about the major cost sources of his/her design. Additionally, some alternative methods for approximating manu-facturing times from empirical data, namely neural networks and local weighted regression, are introduced.

am

[BibTex]

1993


[BibTex]