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2003


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Hierarchical Spatio-Temporal Morphable Models for Representation of complex movements for Imitation Learning

Ilg, W., Bakir, GH., Franz, MO., Giese, M.

In 11th International Conference on Advanced Robotics, (2):453-458, (Editors: Nunes, U., A. de Almeida, A. Bejczy, K. Kosuge and J.A.T. Machado), 11th International Conference on Advanced Robotics, January 2003 (inproceedings)

ei

PDF [BibTex]

2003


PDF [BibTex]


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Feature Selection for Support Vector Machines by Means of Genetic Algorithms

Fröhlich, H., Chapelle, O., Schölkopf, B.

In 15th IEEE International Conference on Tools with AI, pages: 142-148, 15th IEEE International Conference on Tools with AI, 2003 (inproceedings)

ei

[BibTex]

[BibTex]


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Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

Quiñonero-Candela, J., Girard, A., Larsen, J., Rasmussen, CE.

In IEEE International Conference on Acoustics, Speech and Signal Processing, 2, pages: 701-704, IEEE International Conference on Acoustics, Speech and Signal Processing, 2003 (inproceedings)

Abstract
The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables improved estimates of forecasted values and their uncertainties. In this paper we focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaussian Process and the Relevance Vector Machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting. The capability of the method is demonstrated for forecasting of time-series and compared to approximate methods.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Unsupervised Clustering of Images using their Joint Segmentation

Seldin, Y., Starik, S., Werman, M.

In The 3rd International Workshop on Statistical and Computational Theories of Vision (SCTV 2003), pages: 1-24, 3rd International Workshop on Statistical and Computational Theories of Vision (SCTV), 2003 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Kernel Methods and Their Applications to Signal Processing

Bousquet, O., Perez-Cruz, F.

In Proceedings. (ICASSP ‘03), Special Session on Kernel Methods, pages: 860 , ICASSP, 2003 (inproceedings)

Abstract
Recently introduced in Machine Learning, the notion of kernels has drawn a lot of interest as it allows to obtain non-linear algorithms from linear ones in a simple and elegant manner. This, in conjunction with the introduction of new linear classification methods such as the Support Vector Machines has produced significant progress. The successes of such algorithms is now spreading as they are applied to more and more domains. Many Signal Processing problems, by their non-linear and high-dimensional nature may benefit from such techniques. We give an overview of kernel methods and their recent applications.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Predictive control with Gaussian process models

Kocijan, J., Murray-Smith, R., Rasmussen, CE., Likar, B.

In Proceedings of IEEE Region 8 Eurocon 2003: Computer as a Tool, pages: 352-356, (Editors: Zajc, B. and M. Tkal), Proceedings of IEEE Region 8 Eurocon: Computer as a Tool, 2003 (inproceedings)

Abstract
This paper describes model-based predictive control based on Gaussian processes.Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Distance-based classification with Lipschitz functions

von Luxburg, U., Bousquet, O.

In Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, pages: 314-328, (Editors: Schölkopf, B. and M.K. Warmuth), Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory, 2003 (inproceedings)

Abstract
The goal of this article is to develop a framework for large margin classification in metric spaces. We want to find a generalization of linear decision functions for metric spaces and define a corresponding notion of margin such that the decision function separates the training points with a large margin. It will turn out that using Lipschitz functions as decision functions, the inverse of the Lipschitz constant can be interpreted as the size of a margin. In order to construct a clean mathematical setup we isometrically embed the given metric space into a Banach space and the space of Lipschitz functions into its dual space. Our approach leads to a general large margin algorithm for classification in metric spaces. To analyze this algorithm, we first prove a representer theorem. It states that there exists a solution which can be expressed as linear combination of distances to sets of training points. Then we analyze the Rademacher complexity of some Lipschitz function classes. The generality of the Lipschitz approach can be seen from the fact that several well-known algorithms are special cases of the Lipschitz algorithm, among them the support vector machine, the linear programming machine, and the 1-nearest neighbor classifier.

ei

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Bayesian backfitting

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

In Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003), Irvine, CA, May 2003, 2003, clmc (inproceedings)

Abstract
We present an algorithm aimed at addressing both computational and analytical intractability of Bayesian regression models which operate in very high-dimensional, usually underconstrained spaces. Several domains of research frequently provide such datasets, including chemometrics [2], and human movement analysis [1]. The literature in nonparametric statistics provides interesting solutions such as Backfitting [3] and Partial Least Squares [4], which are extremely robust and efficient, yet lack a probabilistic interpretation that could place them in the context of current research in statistical learning algorithms that emphasize the estimation of confidence, posterior distributions, and model complexity. In order to achieve numerical robustness and low computational cost, we first derive a novel Bayesian interpretation of Backfitting (BB) as a computationally efficient regression algorithm. BBÕs learning complexity scales linearly with the input dimensionality by decoupling inference among individual input dimensions. We embed BB in an efficient, locally variational model selection mechanism that automatically grows the number of backfitting experts in a mixture-of-experts regression model. We demonstrate the effectiveness of the algorithm in performing principled regularization of model complexity when fitting nonlinear manifolds while avoiding the numerical hazards associated with highly underconstrained problems. We also note that this algorithm appears applicable in various areas of neural computation, e.g., in abstract models of computational neuroscience, or implementations of statistical learning on artificial systems.

am

link (url) [BibTex]

link (url) [BibTex]


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Reinforcement learning for humanoid robotics

Peters, J., Vijayakumar, S., Schaal, S.

In IEEE-RAS International Conference on Humanoid Robots (Humanoids2003), Karlsruhe, Germany, Sept.29-30, 2003, clmc (inproceedings)

Abstract
Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, `vanilla' policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. `Vanilla' policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade (2002) is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high dimensionally continuous state-action systems.

am

link (url) [BibTex]

link (url) [BibTex]


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Grain boundary phase transitions in the Al-Mg system and their influence on high-strain rate superplasticity

Straumal, B. B., Lopez, G. A., Mittemeijer, E. J., Gust, W., Zhilyaev, A. P.

In 216-217, pages: 307-312, Moscow, Russia, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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High aspect ratio polymer micro/nano-structure manufacturing using nanoembossing, nanomolding and directed self-assembly

Sitti, M.

In ASME 2003 International Mechanical Engineering Congress and Exposition, pages: 293-297, 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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Nsf workshop on future directions in nano-scale systems, dynamics and control

Sitti, M.

In Automatic Control Conference (ACC), 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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3-D nano-fiber manufacturing by controlled pulling of liquid polymers using nano-probes

Nain, A. S., Sitti, M.

In Nanotechnology, 2003. IEEE-NANO 2003. 2003 Third IEEE Conference on, 1, pages: 60-63, 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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Discovering imitation strategies through categorization of multi-cimensional data

Billard, A., Epars, Y., Schaal, S., Cheng, G.

In IEEE International Conference on Intelligent Robots and Systems (IROS 2003), Las Vegas, NV, Oct. 27-31, 2003, clmc (inproceedings)

Abstract
An essential problem of imitation is that of determining Ówhat to imitateÓ, i.e. to determine which of the many features of the demonstration are relevant to the task and which should be reproduced. The strategy followed by the imitator can be modeled as a hierarchical optimization system, which minimizes the discrepancy between two multidimensional datasets. We consider imitation of a manipulation task. To classify across manipulation strategies, we apply a probabilistic analysis to data in Cartesian and joint spaces. We determine a general metric that optimizes the policy of task reproduction, following strategy determination. The model successfully discovers strategies in six different manipulation tasks and controls task reproduction by a full body humanoid robot. or the complete path followed by the demonstrator. We follow a similar taxonomy and apply it to the learning and reproduction of a manipulation task by a humanoid robot. We take the perspective that the features of the movements to imitate are those that appear most frequently, i.e. the invariants in time. The model builds upon previous work [3], [4] and is composed of a hierarchical time delay neural network that extracts invariant features from a manipulation task performed by a human demonstrator. The system analyzes the Carthesian trajectories of the objects and the joint

am

link (url) [BibTex]

link (url) [BibTex]


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Scaling reinforcement learning paradigms for motor learning

Peters, J., Vijayakumar, S., Schaal, S.

In Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003), Irvine, CA, May 2003, 2003, clmc (inproceedings)

Abstract
Reinforcement learning offers a general framework to explain reward related learning in artificial and biological motor control. However, current reinforcement learning methods rarely scale to high dimensional movement systems and mainly operate in discrete, low dimensional domains like game-playing, artificial toy problems, etc. This drawback makes them unsuitable for application to human or bio-mimetic motor control. In this poster, we look at promising approaches that can potentially scale and suggest a novel formulation of the actor-critic algorithm which takes steps towards alleviating the current shortcomings. We argue that methods based on greedy policies are not likely to scale into high-dimensional domains as they are problematic when used with function approximation Ð a must when dealing with continuous domains. We adopt the path of direct policy gradient based policy improvements since they avoid the problems of unstabilizing dynamics encountered in traditional value iteration based updates. While regular policy gradient methods have demonstrated promising results in the domain of humanoid notor control, we demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. Based on this, it is proved that KakadeÕs Ôaverage natural policy gradientÕ is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges with probability one to the nearest local minimum in Riemannian space of the cost function. The algorithm outperforms nonnatural policy gradients by far in a cart-pole balancing evaluation, and offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.

am

link (url) [BibTex]

link (url) [BibTex]


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Influence of grain boundary phase transitions on the diffusion-related properties

Straumal, B., Baretzky, B.

In Proceedings of the International Conference on Diffusion, Segregation and Stresses in Materials, pages: 53-64, Defect and Diffusion Forum, Scitec Publications Ltd., Moscow, Russia, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Are carbon nanostructures an efficient hydrogen storage medium?

Hirscher, M., Becher, M., Haluska, M., von Zeppelin, F., Chen, X., Dettlaff-Weglikowska, U., Roth, S.

In 356-357, pages: 433-437, Annecy, France, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Learning attractor landscapes for learning motor primitives

Ijspeert, A., Nakanishi, J., Schaal, S.

In Advances in Neural Information Processing Systems 15, pages: 1547-1554, (Editors: Becker, S.;Thrun, S.;Obermayer, K.), Cambridge, MA: MIT Press, 2003, clmc (inproceedings)

Abstract
If globally high dimensional data has locally only low dimensional distributions, it is advantageous to perform a local dimensionality reduction before further processing the data. In this paper we examine several techniques for local dimensionality reduction in the context of locally weighted linear regression. As possible candidates, we derive local versions of factor analysis regression, principle component regression, principle component regression on joint distributions, and partial least squares regression. After outlining the statistical bases of these methods, we perform Monte Carlo simulations to evaluate their robustness with respect to violations of their statistical assumptions. One surprising outcome is that locally weighted partial least squares regression offers the best average results, thus outperforming even factor analysis, the theoretically most appealing of our candidate techniques.Ê

am

link (url) [BibTex]

link (url) [BibTex]


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Manufacturing of two and three-dimensional micro/nanostructures by integrating optical tweezers with chemical assembly

Castelino, K., Satyanarayana, S., Sitti, M.

In Nanotechnology, 2003. IEEE-NANO 2003. 2003 Third IEEE Conference on, 1, pages: 56-59, 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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Grain boundary faceting phase transition and thermal grooving in Cu

Straumal, B. B., Polyakov, S. A., Bischoff, E., Mittemeijer, E. J., Gust, W.

In Proceedings of the International Conference on Diffusion, Segregation and Stresses in Materials, 216/217, pages: 93-100, Diffusion and Defect Data, Pt. A, Defect and Diffusion Forum, Scitec Publ., Moscow, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Evolution of Fault-tolerant Self-replicating Structures

Righetti, L., Shokur, S., Capcarre, M.

In Advances in Artificial Life, pages: 278-288, Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2003 (inproceedings)

Abstract
Designed and evolved self-replicating structures in cellular automata have been extensively studied in the past as models of Artificial Life. However, CAs, unlike their biological counterpart, are very brittle: any faulty cell usually leads to the complete destruction of any emerging structures, let alone self-replicating structures. A way to design fault-tolerant structures based on error-correcting-code has been presented recently [1], but it required a cumbersome work to be put into practice. In this paper, we get back to the original inspiration for these works, nature, and propose a way to evolve self-replicating structures, faults here being only an idiosyncracy of the environment.

mg

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learning from demonstration and adaptation of biped locomotion with dynamical movement primitives

Nakanishi, J., Morimoto, J., Endo, G., Schaal, S., Kawato, M.

In Workshop on Robot Learning by Demonstration, IEEE International Conference on Intelligent Robots and Systems (IROS 2003), Las Vegas, NV, Oct. 27-31, 2003, clmc (inproceedings)

Abstract
In this paper, we report on our research for learning biped locomotion from human demonstration. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like locomotion. We suggest dynamical movement primitives as a CPG of a biped robot, an approach we have previously proposed for learning and encoding complex human movements. Demonstrated trajectories are learned through the movement primitives by locally weighted regression, and the frequency of the learned trajectories is adjusted automatically by a novel frequency adaptation algorithm based on phase resetting and entrainment of oscillators. Numerical simulations demonstrate the effectiveness of the proposed locomotion controller.

am

link (url) [BibTex]

link (url) [BibTex]


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Movement planning and imitation by shaping nonlinear attractors

Schaal, S.

In Proceedings of the 12th Yale Workshop on Adaptive and Learning Systems, Yale University, New Haven, CT, 2003, clmc (inproceedings)

Abstract
Given the continuous stream of movements that biological systems exhibit in their daily activities, an account for such versatility and creativity has to assume that movement sequences consist of segments, executed either in sequence or with partial or complete overlap. Therefore, a fundamental question that has pervaded research in motor control both in artificial and biological systems revolves around identifying movement primitives (a.k.a. units of actions, basis behaviors, motor schemas, etc.). What are the fundamental building blocks that are strung together, adapted to, and created for ever new behaviors? This paper summarizes results that led to the hypothesis of Dynamic Movement Primitives (DMP). DMPs are units of action that are formalized as stable nonlinear attractor systems. They are useful for autonomous robotics as they are highly flexible in creating complex rhythmic (e.g., locomotion) and discrete (e.g., a tennis swing) behaviors that can quickly be adapted to the inevitable perturbations of a dy-namically changing, stochastic environment. Moreover, DMPs provide a formal framework that also lends itself to investigations in computational neuroscience. A recent finding that allows creating DMPs with the help of well-understood statistical learning methods has elevated DMPs from a more heuristic to a principled modeling approach, and, moreover, created a new foundation for imitation learning. Theoretical insights, evaluations on a humanoid robot, and behavioral and brain imaging data will serve to outline the framework of DMPs for a general approach to motor control and imitation in robotics and biology.

am

link (url) [BibTex]

link (url) [BibTex]


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Synthetic gecko foot-hair micro/nano-structures for future wall-climbing robots

Sitti, M., Fearing, R. S.

In Robotics and Automation, 2003. Proceedings. ICRA’03. IEEE International Conference on, 1, pages: 1164-1170, 2003 (inproceedings)

pi

[BibTex]

[BibTex]


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Grain boundary faceting phase transition and thermal grooving in Cu

Straumal, B. B., Polyakov, S. A., Bischoff, E., Mittemeijer, E. J., Gust, W.

In Proceedings of the International Conference on Diffusion, Segregation and Stresses in Materials, 216/217, pages: 93-100, Diffusion and Defect Data, Pt. A, Defect and Diffusion Forum, Scitec Publ., Moscow, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Biomimetic propulsion for a swimming surgical micro-robot

Edd, J., Payen, S., Rubinsky, B., Stoller, M. L., Sitti, M.

In Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on, 3, pages: 2583-2588, 2003 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Coercivity mechanism in nanocrystalline and bonded magnets

Goll, D., Kronmüller, H.

In Bonded Magnets. Proceedings of the NATO Advanced Research Workshop on Science and Technology of Bonded Magnets, 118, pages: 115-127, NATO Science Series: Series 2, Mathematics, Physics and Chemistry, Kluwer Acad. Publ., Newark, USA, 2003 (inproceedings)

mms

[BibTex]

[BibTex]


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Investigation of Electromigration in Copper Interconnects by Noise Measurements

Emelianov, V., Ganesan, G., Puzic, A., Schulz, S., Eizenberg, M., Habermeier, H., Stoll, H.

In Noise as a Tool for Studying Materials, pages: 271-281, Proceedings of SPIE, Santa Fe, New Mexico, 2003 (inproceedings)

mms

[BibTex]

[BibTex]

1998


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Programmable pattern generators

Schaal, S., Sternad, D.

In 3rd International Conference on Computational Intelligence in Neuroscience, pages: 48-51, Research Triangle Park, NC, Oct. 24-28, October 1998, clmc (inproceedings)

Abstract
This paper explores the idea to create complex human-like arm movements from movement primitives based on nonlinear attractor dynamics. Each degree-of-freedom of an arm is assumed to have two independent abilities to create movement, one through a discrete dynamic system, and one through a rhythmic system. The discrete system creates point-to-point movements based on internal or external target specifications. The rhythmic system can add an additional oscillatory movement relative to the current position of the discrete system. In the present study, we develop appropriate dynamic systems that can realize the above model, motivate the particular choice of the systems from a biological and engineering point of view, and present simulation results of the performance of such movement primitives. Implementation results on a Sarcos Dexterous Arm are discussed.

am

link (url) [BibTex]

1998


link (url) [BibTex]


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Prior knowledge in support vector kernels

Schölkopf, B., Simard, P., Smola, A., Vapnik, V.

In Advances in Neural Information Processing Systems 10, pages: 640-646 , (Editors: M Jordan and M Kearns and S Solla ), MIT Press, Cambridge, MA, USA, Eleventh Annual Conference on Neural Information Processing (NIPS), June 1998 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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From regularization operators to support vector kernels

Smola, A., Schölkopf, B.

In Advances in Neural Information Processing Systems 10, pages: 343-349, (Editors: M Jordan and M Kearns and S Solla), MIT Press, Cambridge, MA, USA, 11th Annual Conference on Neural Information Processing (NIPS), June 1998 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Robust local learning in high dimensional spaces

Vijayakumar, S., Schaal, S.

In 5th Joint Symposium on Neural Computation, pages: 186-193, Institute for Neural Computation, University of California, San Diego, San Diego, CA, 1998, clmc (inproceedings)

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 this 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

[BibTex]

[BibTex]


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Nano tele-manipulation using virtual reality interface

Sitti, M., Horiguchi, S., Hashimoto, H.

In Industrial Electronics, 1998. Proceedings. ISIE’98. IEEE International Symposium on, 1, pages: 171-176, 1998 (inproceedings)

pi

[BibTex]

[BibTex]


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Local dimensionality reduction

Schaal, S., Vijayakumar, S., Atkeson, C. G.

In Advances in Neural Information Processing Systems 10, pages: 633-639, (Editors: Jordan, M. I.;Kearns, M. J.;Solla, S. A.), MIT Press, Cambridge, MA, 1998, clmc (inproceedings)

Abstract
If globally high dimensional data has locally only low dimensional distributions, it is advantageous to perform a local dimensionality reduction before further processing the data. In this paper we examine several techniques for local dimensionality reduction in the context of locally weighted linear regression. As possible candidates, we derive local versions of factor analysis regression, principle component regression, principle component regression on joint distributions, and partial least squares regression. After outlining the statistical bases of these methods, we perform Monte Carlo simulations to evaluate their robustness with respect to violations of their statistical assumptions. One surprising outcome is that locally weighted partial least squares regression offers the best average results, thus outperforming even factor analysis, the theoretically most appealing of our candidate techniques.

am

link (url) [BibTex]

link (url) [BibTex]


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Tele-nanorobotics using atomic force microscope

Sitti, M., Hashimoto, H.

In Intelligent Robots and Systems, 1998. Proceedings., 1998 IEEE/RSJ International Conference on, 3, pages: 1739-1746, 1998 (inproceedings)

pi

[BibTex]

[BibTex]


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Biomimetic gaze stabilization based on a study of the vestibulocerebellum

Shibata, T., Schaal, S.

In European Workshop on Learning Robots, pages: 84-94, Edinburgh, UK, 1998, clmc (inproceedings)

Abstract
Accurate oculomotor control is one of the essential pre-requisites for successful visuomotor coordination. In this paper, we suggest a biologically inspired control system for learning gaze stabilization with a biomimetic robotic oculomotor system. In a stepwise fashion, we develop a control circuit for the vestibulo-ocular reflex (VOR) and the opto-kinetic response (OKR), and add a nonlinear learning network to allow adaptivity. We discuss the parallels and differences of our system with biological oculomotor control and suggest solutions how to deal with nonlinearities and time delays in the control system. In simulation and actual robot studies, we demonstrate that our system can learn gaze stabilization in real time in only a few seconds with high final accuracy.

am

link (url) [BibTex]

link (url) [BibTex]


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2D micro particle assembly using atomic force microscope

Sitti, M., Hirahara, K., Hashimoto, H.

In Micromechatronics and Human Science, 1998. MHS’98. Proceedings of the 1998 International Symposium on, pages: 143-148, 1998 (inproceedings)

pi

[BibTex]

[BibTex]


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Towards biomimetic vision

Shibata, T., Schaal, S.

In International Conference on Intelligence Robots and Systems, pages: 872-879, Victoria, Canada, 1998, clmc (inproceedings)

Abstract
Oculomotor control is the foundation of most biological visual systems, as well as an important component in the entire perceptual-motor system. We review some of the most basic principles of biological oculomotor systems, and explore their usefulness from both the biological and computational point of view. As an example of biomimetic oculomotor control, we present the state of our implementations and experimental results using the vestibulo-ocular-reflex and opto-kinetic-reflex paradigm

am

link (url) [BibTex]

link (url) [BibTex]


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Macro to nano tele-manipulation through nanoelectromechanical systems

Sitti, M., Hashimoto, H.

In Industrial Electronics Society, 1998. IECON’98. Proceedings of the 24th Annual Conference of the IEEE, 1, pages: 98-103, 1998 (inproceedings)

pi

[BibTex]

[BibTex]


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Geometric Image Synthesis

Alhaija, H. A., Mustikovela, S. K., Geiger, A., Rother, C.

(conference)

avg

Project Page [BibTex]

Project Page [BibTex]