René Felix Reinhart

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We present a connectionist approach to learn forward and redundant inverse kinematics in a single recurrent network. The network architecture extends the reservoir computing idea, i.e. to read out the state of a fixed dynamic system, into an associative setting, which learns the forward and backward mapping simultaneously. For output learning we use(More)
We introduce a novel regularization approach for a class of inputdriven recurrent neural networks. The regularization of network parameters is constrained to reimplement a previously recorded state trajectory. We derive a closed-form solution for network regularization and show that the method is capable of reimplementing harvested dynamics. We investigate(More)
The paper presents a modular architecture for bi-manual skill acquisition from kinesthetic teaching. Skills are learned and embedded over several representational levels comprising a compact movement representation by means of movement primitives, a task space description of the bi-manual tool constraint, and the particular redundancy resolution of the(More)
The data-driven approximation of vector fields that encode dynamical systems is a persistently hard task in machine learning. If data is sparse and given in form of velocities derived from few trajectories only, state-space regions exists, where no information on the vector field and its induced dynamics is available. Generalization towards such regions is(More)
Bionic soft robots offer exciting perspectives for more flexible and safe physical interaction with the world and humans. Unfortunately, their hardware design often prevents analytical modeling, which in turn is a prerequisite to apply classical automatic control approaches. On the other hand, also modeling by means of learning is hardly feasible due to(More)
Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by(More)
We introduce a novel control framework based on a recurrent neural network for reaching movement generation. The network first learns forward and inverse kinematics, i.e. to associate end effector coordinates with joint angles, by means of attractor states. Modulating the attractor states with the desired target input allows generalization of the learned(More)
We present an efficient online learning scheme for non-negative sparse coding in autoencoder neural networks. It comprises a novel synaptic decay rule that ensures non-negative weights in combination with an intrinsic self-adaptation rule that optimizes sparseness of the non-negative encoding. We show that non-negativity constrains the space of solutions(More)
Output feedback is crucial for autonomous and parameterized pattern generation with reservoir networks. Read-out learning can lead to error amplification in these settings and therefore regularization is important for both generalization and reduction of error amplification. We show that regularization of the inner reservoir network mitigates parameter(More)