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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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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]

1999


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Is imitation learning the route to humanoid robots?

Schaal, S.

Trends in Cognitive Sciences, 3(6):233-242, 1999, clmc (article)

Abstract
This review will focus on two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It will be postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. This hope is justified because imitation learning channels research efforts towards three important issues: efficient motor learning, the connection between action and perception, and modular motor control in form of movement primitives. In order to make these points, first, a brief review of imitation learning will be given from the view of psychology and neuroscience. In these fields, representations and functional connections between action and perception have been explored that contribute to the understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution provided a first idea of the possible neural basis of imitation. Secondly, computational approaches to imitation learning will be described, initially from the perspective of traditional AI and robotics, and then with a focus on neural network models and statistical learning research. Parallels and differences between biological and computational approaches to imitation will be highlighted. The review will end with an overview of current projects that actually employ imitation learning for humanoid robots.

am

link (url) [BibTex]

1999


link (url) [BibTex]


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Nonparametric regression for learning nonlinear transformations

Schaal, S.

In Prerational Intelligence in Strategies, High-Level Processes and Collective Behavior, 2, pages: 595-621, (Editors: Ritter, H.;Cruse, H.;Dean, J.), Kluwer Academic Publishers, 1999, clmc (inbook)

Abstract
Information processing in animals and artificial movement systems consists of a series of transformations that map sensory signals to intermediate representations, and finally to motor commands. Given the physical and neuroanatomical differences between individuals and the need for plasticity during development, it is highly likely that such transformations are learned rather than pre-programmed by evolution. Such self-organizing processes, capable of discovering nonlinear dependencies between different groups of signals, are one essential part of prerational intelligence. While neural network algorithms seem to be the natural choice when searching for solutions for learning transformations, this paper will take a more careful look at which types of neural networks are actually suited for the requirements of an autonomous learning system. The approach that we will pursue is guided by recent developments in learning theory that have linked neural network learning to well established statistical theories. In particular, this new statistical understanding has given rise to the development of neural network systems that are directly based on statistical methods. One family of such methods stems from nonparametric regression. This paper will compare nonparametric learning with the more widely used parametric counterparts in a non technical fashion, and investigate how these two families differ in their properties and their applicabilities. We will argue that nonparametric neural networks offer a set of characteristics that make them a very promising candidate for on-line learning in autonomous system.

am

link (url) [BibTex]

link (url) [BibTex]


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Segmentation of endpoint trajectories does not imply segmented control

Sternad, D., Schaal, D.

Experimental Brain Research, 124(1):118-136, 1999, clmc (article)

Abstract
While it is generally assumed that complex movements consist of a sequence of simpler units, the quest to define these units of action, or movement primitives, still remains an open question. In this context, two hypotheses of movement segmentation of endpoint trajectories in 3D human drawing movements are re-examined: (1) the stroke-based segmentation hypothesis based on the results that the proportionality coefficient of the 2/3 power law changes discontinuously with each new â??strokeâ?, and (2) the segmentation hypothesis inferred from the observation of piecewise planar endpoint trajectories of 3D drawing movements. In two experiments human subjects performed a set of elliptical and figure-8 patterns of different sizes and orientations using their whole arm in 3D. The kinematic characteristics of the endpoint trajectories and the seven joint angles of the arm were analyzed. While the endpoint trajectories produced similar segmentation features as reported in the literature, analyses of the joint angles show no obvious segmentation but rather continuous oscillatory patterns. By approximating the joint angle data of human subjects with sinusoidal trajectories, and by implementing this model on a 7-degree-of-freedom anthropomorphic robot arm, it is shown that such a continuous movement strategy can produce exactly the same features as observed by the above segmentation hypotheses. The origin of this apparent segmentation of endpoint trajectories is traced back to the nonlinear transformations of the forward kinematics of human arms. The presented results demonstrate that principles of discrete movement generation may not be reconciled with those of rhythmic movement as easily as has been previously suggested, while the generalization of nonlinear pattern generators to arm movements can offer an interesting alternative to approach the question of units of action.

am

link (url) [BibTex]

link (url) [BibTex]

1996


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A kendama learning robot based on a dynamic optimiation principle

Miyamoto, H., Gandolfo, F., Gomi, H., Schaal, S., Koike, Y., Rieka, O., Nakano, E., Wada, Y., Kawato, M.

In Preceedings of the International Conference on Neural Information Processing, pages: 938-942, Hong Kong, September 1996, clmc (inproceedings)

am

[BibTex]

1996


[BibTex]


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A Kendama learning robot based on bi-directional theory

Miyamoto, H., Schaal, S., Gandolfo, F., Koike, Y., Osu, R., Nakano, E., Wada, Y., Kawato, M.

Neural Networks, 9(8):1281-1302, 1996, clmc (article)

Abstract
A general theory of movement-pattern perception based on bi-directional theory for sensory-motor integration can be used for motion capture and learning by watching in robotics. We demonstrate our methods using the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has a very similar kinematic structure to the human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients are (1) to extract via-points from a human movement trajectory using a forward-inverse relaxation model, (2) to treat via-points as a control variable while reconstructing the desired trajectory from all the via-points, and (3) to modify the via-points for successful execution. In order to test the validity of the via-point representation, we utilized a numerical model of the SARCOS arm, and examined the behavior of the system under several conditions.

am

link (url) [BibTex]

link (url) [BibTex]


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From isolation to cooperation: An alternative of a system of experts

Schaal, S., Atkeson, C. G.

In Advances in Neural Information Processing Systems 8, pages: 605-611, (Editors: Touretzky, D. S.;Mozer, M. C.;Hasselmo, M. E.), MIT Press, Cambridge, MA, 1996, clmc (inbook)

Abstract
We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for data during learning. Only when a prediction for a query is required do the experts cooperate by blending their individual predictions. Each expert is trained by minimizing a penalized local cross validation error using second order methods. In this way, an expert is able to adjust the size and shape of the receptive field in which its predictions are valid, and also to adjust its bias on the importance of individual input dimensions. The size and shape adjustment corresponds to finding a local distance metric, while the bias adjustment accomplishes local dimensionality reduction. We derive asymptotic results for our method. In a variety of simulations we demonstrate the properties of the algorithm with respect to interference, learning speed, prediction accuracy, feature detection, and task oriented incremental learning. 

am

link (url) [BibTex]

link (url) [BibTex]


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One-handed juggling: A dynamical approach to a rhythmic movement task

Schaal, S., Sternad, D., Atkeson, C. G.

Journal of Motor Behavior, 28(2):165-183, 1996, clmc (article)

Abstract
The skill of rhythmic juggling a ball on a racket is investigated from the viewpoint of nonlinear dynamics. The difference equations that model the dynamical system are analyzed by means of local and non-local stability analyses. These analyses yield that the task dynamics offer an economical juggling pattern which is stable even for open-loop actuator motion. For this pattern, two types of pre dictions are extracted: (i) Stable periodic bouncing is sufficiently characterized by a negative acceleration of the racket at the moment of impact with the ball; (ii) A nonlinear scaling relation maps different juggling trajectories onto one topologically equivalent dynamical system. The relevance of these results for the human control of action was evaluated in an experiment where subjects performed a comparable task of juggling a ball on a paddle. Task manipulations involved different juggling heights and gravity conditions of the ball. The predictions were confirmed: (i) For stable rhythmic performance the paddle's acceleration at impact is negative and fluctuations of the impact acceleration follow predictions from global stability analysis; (ii) For each subject, the realizations of juggling for the different experimental conditions are related by the scaling relation. These results allow the conclusion that for the given task, humans reliably exploit the stable solutions inherent to the dynamics of the task and do not overrule these dynamics by other control mechanisms. The dynamical scaling serves as an efficient principle to generate different movement realizations from only a few parameter changes and is discussed as a dynamical formalization of the principle of motor equivalence.

am

link (url) [BibTex]

link (url) [BibTex]