Modeling Joint Synergies to Synthesize Realistic Movements
This paper presents a new method to generate arm gestures which reproduces the dynamical properties of human movements. We describe a model of synergy, defined as a coordinative structure responsible for the flexible organization of joints over time when performing a movement. We propose a generic method which incorporates this synergy model into a motion controller system based on any iterative inverse kinematics technique. We show that this method is independent of the task and can be parametrized to suit an individual using a novel learning algorithm based on a motion capture database. The method yields different models of synergies for reaching tasks that are confronted to the same set of example motions. The quantitative results obtained allow us to select the best model of synergies for reaching movements and prove that our method is independent of the inverse kinematic technique used for the motion controller.
KeywordsVirtual Humanoids Movement Synthesis Synergy Reaching Gesture Joint Synergies Movement Learning
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