This paper presents studies of the coordination of volun-tary human arm movements. A mathematical model is for-mulated which is shown to predict both the qualitative fea-tures and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled mathematically by defining an objective function, a measure of performance for any possi-ble movement. The unique trajectory which yields the best performance is determined using dynamic optimization the-ory. In the work presented here, the objective function is the square of the magnitude of jerk (rate of change of accelera-tion) of the hand integrated over the entire movement. This is equivalent to assuming that a major goal of motor coordi-nation is the production of the smoothest possible movement

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The sensorimotor integration system can be viewed as an observer attempting to estimate its own state and the state of the environment by integrating multiple sources of information. We describe a computational framework capturing this notion, and some specific models of integration and adaptation that result from it. Psychophysical results from two sensorimotor systems, subserving the integration and adaptation of visuo-auditory maps, and estimation of the state of the hand during arm movements, are presented and analyzed within this framework. These results suggest that: (1) Spatial information from visual and auditory systems is integrated so as to reduce the variance in localization. (2) The effects of a remapping in the relation between visual and auditory space can be predicted from a simple learning rule. (3) The temporal propagation of errors in estimating the hand's state is captured by a linear dynamic observer, providing evidence for the existence of an intern...

A stochastic optimized-submovement model is proposed for Pitts ' law, the classic logarithmic tradeoff between the duration and spatial precision of rapid aimed movements. According to the model, an aimed movement toward a specified target region involves a primary submovement and an optional secondary corrective submovement. The submovements are assumed to be programmed such that they minimize average total movement time while maintaining a high frequency of target hits. The programming process achieves this minimization by optimally adjusting the average magnitudes and durations of noisy neuromotor force pulses used to generate the submovements. Numerous results from the literature on human motor performance may be explained in these terms. Two new experiments on rapid wrist rotations yield additional support for the stochastic optimizedsubmovement model. Experiment 1 revealed that the mean durations of primary submovements and of secondary submovements, not just average total movement times, conform to a square-root approximation of Pitts ' law derived from the model. Also, the spatial endpoints of primary submovements have standard deviations that increase linearly with average primary-submovement velocity, and the average primary-submovement velocity influences the relative frequencies of secondary submovements, as predicted by the model. During Experiment 2, these results were replicated and

The authors implemented a system which performs a fundamental visuomotor coordination task on the humanoid robot Cog. Cog's task was to saccade its pair of two degree-offreedom eyes to foveate on a target, and then to maneuver its six degree-of-freedom compliant arm to point at that target. This task requires systems for learning to saccade to visual targets, generating smooth arm trajectories, locating the arm in the visual field, and learning the map between gaze direction and correct pointing configuration of the arm. All learning was self-supervised solely by visual feedback. The task was accomplished by many parallel processes running on a seven processor, extensible architecture, MIMD computer. 1 Introduction This paper is one of a series of developmental snapshots from the Cog Project at the MIT Artificial Intelligence Laboratory. Cog is a humanoid robot designed to explore a wide variety of problems in artificial intelligence and cognitive science (Brooks & Stein 1994). To da...

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. We discuss the tradeoffs involved in control of complex articulated agents, and present three implemented controllers for a complex task: a physically-based humanoid torso dancing the Macarena. The three controllers are drawn from animation, biological models, and robotics, and illustrate the issues of joint-space vs. Cartesian space task specification and implementation. We evaluate the controllers along several qualitative and quantitative dimensions, considering naturalness of movement and controller flexibility. Finally, we propose a general combination approach to control, aimed at utilizing the strengths of each alternative within a general framework for addressing complex motor control of articulated agents. Key words: articulated agent control, motor control, robotics, animation 1. Introduction Control of humanoid agents, dynamically simulated or physical, is an extremely difficult problem due to the high dimensionality of the control space, i.e., the many degrees of freed...

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Trajectory and kinematics of drawing movements are mutually constrained by functional relationships that reduce the degrees of freedom of the hand-arm system. Previous investigations of these relationships are extended here by considering their development in children between 5 and 12 years of age. Performances in a simple motor task—the continuous tracing of elliptic trajectories—demonstrate that both the phenomenon of isochrony (increase of the average movement velocity with the linear extent of the trajectory) and the so-called two-thirds power law (relation between tangential velocity and curvature) are qualitatively present already at the age of 5. The quantitative aspects of these regularities evolve with age, however, and steady-state adult performance is not attained even by the oldest children. The power-law formalism developed in previous reports is generalized to encompass these developmental aspects of the control of movement. Two general frameworks are currently available to conceptualize the motor-control problem. Broadly, the two frameworks differ in the answer that they give to the question "Where do form and structure come from? " According to the

Abstract. We present a psychophysical study of human arm movement imitation, and an approach to analyzing the resulting data, which is general enough to be applied to human or humanoid movement analysis. We describe a joint-space based segmentation and comparison algorithm that allows us to evaluate the performance of 11 different subjects performing a series of arm movement imitation tasks. The results provide analytical evidence for the strong interference effects of simultaneous rehearsal during observation. Additionally, the results also demonstrate that repeated imitation in these tasks did not affect the subjects ’ performance. 1