Abstract
We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons’ states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers’ trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings.
Highlights
In recent years, various neural network topologies have been used for recognizing and representing human body motions
Let us compare the approach based on centralized networks, proposed in this present paper, and the classical method of dynamic movement primitives (DMPs)
We have shown that marker trajectories of representative body parts can be approximated well by centralized networks consisting of very few centers as oscillators—2 to 3 oscillators have been shown to be sufficient even for rather complicated motions
Summary
Various neural network topologies have been used for recognizing and representing human body motions. Our method combines nonlinear oscillators, centralized architectures, and approximation by radial basis functions. All these ingredients are present in different fields in neuroscience, robotics, and machine learning, but to the best of our knowledge they have not yet been put together. The interest of our centralized architecture relating a few pacemaker hubs to satellite effectors is manifold Beyond realism, it allows a robust control of body motions. The second part is robust, being based on simple, two-layer, feed-forward radial basis function networks Several extensions of this basic approach imply recursive networks. Due to the network switching module we can use nonlinear oscillators and obtain a global network that can simulate a large class of different motions.
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