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We present a dynamical system approach that couples task and joint space by means of an attractor-based content addressable memory. The respective recurrent reservoir network simultaneously provides a novel control framework for goal directed movement generation. The network first learns to associate end effector coordinates with joint angles by means of reservoir attractor states and thereby implements...
We introduce a control framework based on a recurrent neural network for goal-directed movement generation. We exploit the network dynamics to implement a nonlinear task space controller. Efficient online learning and execution of the network makes the proposed approach adaptive and real-time capable. We achieve reliable and excellent generalization for the 7-DOF redundant PA-10 robot arm in simulation.
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 efficient Backpropagation-Decorrelation learning...
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