On the Influence of Hand Dynamics on Motion Planning of Reaching Movements in Haptic Environments

The paper presents an analysis of human reaching movements in the manipulation of flexible objects. Two models, the minimum hand jerk and the minimum driving hand forcechange, are used for modelling and verification of experimental data. The data are collected with the haptic system supporting dynamic simulation of the flexible object in real time. We describe some initial experimental results and analyze the applicability of the models. It is found that even for short-term movements human motion planning strategy can depend on arm inertia and configuration. This conclusion is based on the experimental evidence of the multi-phased hand velocity profiles that can be well captured by the minimum driving hand force-change criterion. To support the latest observation, an experiment with reinforcement learning was conducted

Optimality Principles and Motion Planning of Human-Like Reaching Movements

The paper deals with modeling of human-like reaching movements. Several issues are under study. First, we consider a model of unconstrained reaching movements that corresponds to the minimization of control effort. It is shown that this model can be represented by the wellknown Beta function. The representation can be used for the construction of fractional order models and also for modeling of asymmetric velocity proï¬les. Next, we address the formation of boundary conditions in a natural way. From the mathematical point of view, the structure of the optimal solution is deï¬ned not only by the form of the optimality criterion but also by the boundary conditions of the optimization task. The natural boundary conditions, deï¬ned in this part of the paper, can also be used in modeling asymmetric velocity proï¬les. Finally, addressing the modeling of reaching movements with bounded control actions, we consider the minimum time formulation of the optimization problem and (for the n-th order integrator) ï¬nd its analytical solution

Reinforcement learningApproach to Objectcontact Motion with Estimation of Lowdimensional Submanifold and Mode-Boundary

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