Models of human movement from computational neuroscience provide a starting point for building a system that can produce flexible adaptive movement on a robot. There have been many computational models of human upper limb movement put forward, each attempting to explain one or more of the stereotypical features that characterize such movements. While these models successfully capture some of the features of human movement, they often lack a compelling biological basis for the criteria they choose to optimize. One that does provide such a basis is the minimum variance model (and its extension—task optimization in the presence of signal‐dependent noise). Here, the variance of the hand position at the end of a movement is minimized, given that the control signals on the arm's actuators are subject to random noise with zero mean and variance proportional to the amplitude of the signal. Since large control signals, required to move fast, would have higher amplitude noise, the speed‐accuracy trade‐off emerges as a direct result of the optimization process. We chose to implement a version of this model that would be suitable for the control of a robot arm, using an optimal control scheme based on the discrete‐time linear quadratic regulator. This implementation allowed us to examine the applicability of the minimum variance model to producing humanlike movement. In this paper, we describe our implementation of the minimum variance model, both for point‐to‐point reaching movements and for more complex trajectories involving via points. We also evaluate its performance in producing humanlike movement and show its advantages over other optimization based models (the well‐known minimum jerk and minimum torque‐change models) for the control of a robot arm. © 2005 Wiley Periodicals, Inc.

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Reaching-to-grasp has generally been classified as the coordination of two separate visuomotor processes: transporting the hand to the target object and performing the grip. An alternative view has recently been formed that grasping can be explained as pointing movements performed by the digits of the hand to target positions on the object. We have previously implemented the minimum variance model of human movement as an optimal control scheme suitable for control of a robot arm reaching to a target. Here, we extend that scheme to perform grasping movements with a hand and arm model. Since the minimum variance model requires that signal-dependent noise be present on the motor commands to the actuators of the movement, our approach is to plan the reach and the grasp separately, in line with the classical view, but using the same computational model for pointing, in line with the alternative view. We show that our model successfully captures some of the key characteristics of human grasping movements, including the observations that maximum grip size increases with object size (with a slope of approximately 0.8) and that this maximum grip occurs at 60-80% of the movement time. We then use our model to analyse contributions to the digit end-point variance from the two components of the grasp (the transport and the grip). We also briefly discuss further areas of investigation that are prompted by our model.

Ahslmcl-Progress in the field of humanoid robotics and of optimality principles to the production of human-like the need to hd-simpler ways to program such robots has prompted research into computational models for robotic learning from human demonstration. To furlher investigate biologjcally inspired human-like robotic movement and hitation, we have constructed a framework based on three key features of human movement and planning: optimalily, modularity and learning. In lhis paper we f m s on the application of optimality principles lo the production of human-like movement by a robot arm. Among computational lhmries of human movement, the signal-dependent noise, or minimum variance, model w'as chosen as a biologically realistic control scheme lo produce human-like movement. A well known optimal control algorithm, lhe linear quadratic regulator, WBS adapted to implement lhis model. The scheme was applied both in simulation and on a real robot ann, which demonstrated human-like movement profiles in a point-to-point reaching experiment.

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