Abstract
Our framework is grasp oriented, intelligent wrist positioning in the approach phase of a multifingered robot hand. This paper focuses on the performance analysis of trainer sets in a multitarget multiobstacle environment. Targets or attractors (obstacles or repellers) are task relevant (irrelevant) parts which are imperfectly recognized. Due to this uncertainty we model the joint epace as a cellular space and due to the attractor repeller multiplicity the workspace of the robot hand is also modeled as a multiwell potential. Learning performance of a given trainer is analyzed under nonlinear robot dynamics in this cell space and in the parameter space. Bifurcation that causes the system to be structurally unstable is studied from the different perspectives of basin erosion, fractal fingering and pitchfork bifurcations which witness periodicity splitting. The effect of bifurcation in the learning rate is also considered
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