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• W2624636562 abstract "Properties of Synergies Arising From a Theory of Optimal Motor Behavior Manu Chhabra (mchhabra@cs.rochester.edu) Department of Computer Science University of Rochester Rochester, NY 14627 USA Robert A. Jacobs (robbie@bcs.rochester.edu) Department of Brain & Cognitive Sciences University of Rochester Rochester, NY 14627 USA Abstract typically have many degrees of freedom, where the de- grees of freedom in a system are the number of di- mensions in which the system can independently vary (Rosenbaum, 1991). The number of degrees of freedom of a system carrying out a task often exceeds the num- ber of degrees of freedom needed to specify the task and, thus, the degrees of freedom are typically redundant (Jordan & Rosenbaum, 1989). For example, consider the problem of touching the tip of your nose. The location of your nose has 3 degrees of freedom (its x, y, and z po- sition in Cartesian coordinates), but the joints of your arm have 7 degrees of freedom (the shoulder has 3 de- grees of freedom, and the elbow and wrist each have 2). As a result, there are several different settings of your arm’s joint positions that all allow you to touch your nose. Which setting should you use? A solution to this problem is to create motor syner- gies, which are dependencies among dimensions of the motor system. For example, a synergy might be a cou- pling of the motions of your shoulder and elbow. Syn- ergies provide two types of benefits to motor systems. First, synergies ameliorate the problem of redundancy; e.g., synergies can constrain the set of possible shoulder, elbow, and wrist positions that allow you to touch your nose. Second, synergies reduce the number of degrees of freedom that must be independently controlled, thereby making it easier to control a motor system (Bernstein, 1967). Because synergies make motor systems easier to control, they are often hypothesized to serve as motor primitives, building blocks, or basis functions. Impor- tantly, they provide basic units of motor behavior that can be linearly combined to form more complex units of behavior. This paper overviews the properties of synergies aris- ing from a theory of optimal motor behavior. A more detailed description of this work, and additional results, can be found in Chhabra & Jacobs (2006). The motor behavior that we studied is the control of a nonlinear dy- namical system, namely a simulated two-joint arm that resembles a human arm. We have created an optimal controller for this arm that formalizes goals as mathe- matical constraints, and searches for control signals that optimize the constraints. This has been done both for reaching tasks (move an end-effector from one point to another) and for via-point tasks (move from one point We consider the properties of motor components, also known as synergies, arising from a computational theory (in the sense of Marr, 1982) or theory of optimal motor behavior. We study this topic in the domain of optimal control of a two-joint arm for reaching and via-point tasks. Studies of the motor synergies revealed several interesting findings. First, optimal motor actions for a new reaching or via-point movement can be generated by summing a small number of scaled and time-shifted motor synergies, indicating that optimal movements can be planned in a low-dimensional space by using optimal motor synergies as motor primitives or building blocks. Second, some optimal synergies are task-independent— they arise regardless of the task context—whereas other synergies are task-dependent—they arise in the context of one task but not in the contexts of other tasks. Bio- logical organisms use a combination of task-independent and task-dependent synergies. Our work suggests that this may be an efficient combination for generating op- timal motor actions from motor primitives. Lastly, op- timal motor actions can be rapidly acquired by learn- ing new linear combinations of optimal motor synergies. This result provides further evidence that optimal motor synergies are useful motor primitives. Keywords: action; motor control; robotics; learning Introduction Marr (1982) hypothesized that a complex information processing device could be analyzed at three levels. The top level, referred to as the computational theory, exam- ines what the device does and why. The analysis at this level provides an explanation for why a device does what it does by studying the device’s goals. Although there are many different ways of developing a computational theory of aspects of human behavior, an increasingly popular way is through optimal models that formalize goals as mathematical constraints or criteria, search for behaviors that optimize the criteria, and compare the optimal behaviors with human behaviors. If there is a close match, then it is hypothesized that people are be- having as they do because they are efficiently satisfying the same goals as were built into the optimal model. This paper studies motor synergies that arise from computational theories (in the sense of Marr, 1982) of motor behavior. To understand motor synergies, it is helpful to first understand the “degrees of freedom” problem (Bernstein, 1967). Biological motor systems" @default.
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• W2624636562 title "Properties of Synergies Arising From a Theory of Optimal Motor Behavior" @default.
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