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2002


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Forward models in visuomotor control

Mehta, B., Schaal, S.

J Neurophysiol, 88(2):942-53, August 2002, clmc (article)

Abstract
In recent years, an increasing number of research projects investigated whether the central nervous system employs internal models in motor control. While inverse models in the control loop can be identified more readily in both motor behavior and the firing of single neurons, providing direct evidence for the existence of forward models is more complicated. In this paper, we will discuss such an identification of forward models in the context of the visuomotor control of an unstable dynamic system, the balancing of a pole on a finger. Pole balancing imposes stringent constraints on the biological controller, as it needs to cope with the large delays of visual information processing while keeping the pole at an unstable equilibrium. We hypothesize various model-based and non-model-based control schemes of how visuomotor control can be accomplished in this task, including Smith Predictors, predictors with Kalman filters, tapped-delay line control, and delay-uncompensated control. Behavioral experiments with human participants allow exclusion of most of the hypothesized control schemes. In the end, our data support the existence of a forward model in the sensory preprocessing loop of control. As an important part of our research, we will provide a discussion of when and how forward models can be identified and also the possible pitfalls in the search for forward models in control.

am

link (url) [BibTex]

2002


link (url) [BibTex]


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Learning rhythmic movements by demonstration using nonlinear oscillators

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

In IEEE International Conference on Intelligent Robots and Systems (IROS 2002), pages: 958-963, Piscataway, NJ: IEEE, Lausanne, Sept.30-Oct.4 2002, 2002, clmc (inproceedings)

Abstract
Locally weighted learning (LWL) is a class of statistical learning techniques that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in real-time learning of complex robot tasks. We discuss two major classes of LWL, memory-based LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional beliefs that LWL methods cannot work well in high-dimensional spaces, we provide new algorithms that have been tested in up to 50 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devil-sticking, pole-balancing of a humanoid robot arm, and inverse-dynamics learning for a seven degree-of-freedom robot.

am

link (url) [BibTex]

link (url) [BibTex]


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Learning robot control

Schaal, S.

In The handbook of brain theory and neural networks, 2nd Edition, pages: 983-987, 2, (Editors: Arbib, M. A.), MIT Press, Cambridge, MA, 2002, clmc (inbook)

Abstract
This is a review article on learning control in robots.

am

link (url) [BibTex]

link (url) [BibTex]


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Arm and hand movement control

Schaal, S.

In The handbook of brain theory and neural networks, 2nd Edition, pages: 110-113, 2, (Editors: Arbib, M. A.), MIT Press, Cambridge, MA, 2002, clmc (inbook)

Abstract
This is a review article on computational and biological research on arm and hand control.

am

link (url) [BibTex]

link (url) [BibTex]


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Scalable techniques from nonparameteric statistics for real-time robot learning

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

Applied Intelligence, 17(1):49-60, 2002, clmc (article)

Abstract
Locally weighted learning (LWL) is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in real-time learning of complex robot tasks. We discuss two major classes of LWL, memory-based LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional belief that LWL methods cannot work well in high-dimensional spaces, we provide new algorithms that have been tested on up to 90 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devil-sticking, pole-balancing by a humanoid robot arm, and inverse-dynamics learning for a seven and a 30 degree-of-freedom robot. In all these examples, the application of our statistical neural networks techniques allowed either faster or more accurate acquisition of motor control than classical control engineering.

am

link (url) [BibTex]

link (url) [BibTex]


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A micromechanical flying insect thorax

Fearing, R., Avadhanula, S., Campolo, D., Sitti, M., Yan, J., Wood, R.

Neurotechnology for Biomimetic Robots, pages: 469-480, The MIT Press Cambridge, MA, 2002 (article)

pi

[BibTex]

[BibTex]


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Movement imitation with nonlinear dynamical systems in humanoid robots

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

In International Conference on Robotics and Automation (ICRA2002), Washinton, May 11-15 2002, 2002, clmc (inproceedings)

Abstract
Locally weighted learning (LWL) is a class of statistical learning techniques that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in real-time learning of complex robot tasks. We discuss two major classes of LWL, memory-based LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional beliefs that LWL methods cannot work well in high-dimensional spaces, we provide new algorithms that have been tested in up to 50 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devil-sticking, pole-balancing of a humanoid robot arm, and inverse-dynamics learning for a seven degree-of-freedom robot.

am

link (url) [BibTex]

link (url) [BibTex]


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A locally weighted learning composite adaptive controller with structure adaptation

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

In IEEE International Conference on Intelligent Robots and Systems (IROS 2002), Lausanne, Sept.30-Oct.4 2002, 2002, clmc (inproceedings)

Abstract
This paper introduces a provably stable adaptive learning controller which 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 pro-posed learning adaptive control algorithm uses both the tracking error and the estimation error to up-date the parameters. We provide Lyapunov analyses that demonstrate the stability properties of the learning controller. Numerical simulations illustrate rapid convergence of the tracking error and the automatic structure adaptation capability of the function approximator. This paper introduces a provably stable adaptive learning controller which 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 pro-posed learning adaptive control algorithm uses both the tracking error and the estimation error to up-date the parameters. We provide Lyapunov analyses that demonstrate the stability properties of the learning controller. Numerical simulations illustrate rapid convergence of the tracking error and the automatic structure adaptation capability of the function approximator

am

link (url) [BibTex]

link (url) [BibTex]


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Nanomolding based fabrication of synthetic gecko foot-hairs

Sitti, M., Fearing, R. S.

In Nanotechnology, 2002. IEEE-NANO 2002. Proceedings of the 2002 2nd IEEE Conference on, pages: 137-140, 2002 (inproceedings)

pi

Project Page [BibTex]

Project Page [BibTex]


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Evidence for van der Waals adhesion in gecko setae

Autumn, K., Sitti, M., Liang, Y. A., Peattie, A. M., Hansen, W. R., Sponberg, S., Kenny, T. W., Fearing, R., Israelachvili, J. N., Full, R. J.

Proceedings of the National Academy of Sciences, 99(19):12252-12256, National Acad Sciences, 2002 (article)

pi

Project Page [BibTex]

Project Page [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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Tele-nanorobotics using an atomic force microscope as a nanorobot and sensor

Sitti, M., Hashimoto, H.

Advanced Robotics, 13(4):417-436, Taylor & Francis, 1998 (article)

pi

[BibTex]

[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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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]

link (url) [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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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]


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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]