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Modular reservoir computing networks for imitation learning of multiple robot behaviors
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Modular reservoir computing networks for imitation learning of multiple robot behaviors   (Citations: 2)
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Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service robotics as well as of learning robots. In this work, we use reservoir computing (RC) for learning robot behaviors by demonstration. In RC, a randomly generated recurrent neural network, the reservoir, projects the input to a dynamic temporal space. The reservoir states are mapped into a readout output layer which is the solely part being trained using standard linear regression. In this paper, we use a two layered modular structure, where the first layer comprises two RC networks, each one for learning primitive behaviors, namely, obstacle avoidance and target seeking. The second layer is composed of one RC network for behavior combination and coordination. The hierarchical RC network learns by examples given by simple controllers which implement the primitive behaviors. We use a simulation model of the e-puck robot which has distance sensors and a camera that serves as input for our system. The experiments show that, after training, the robot learns to coordinate the goal seeking (GS) and the object avoidance (OA) behaviors in unknown environments, being able to capture targets and navigate efficiently.
Conference: Computational Intelligence in Robotics - CIRA , 2009
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    • ...ESNs facilities the practical application of RNNs and outperforms classical fully trained RNNs in many tasks [8,9,10]...

    Qingsong Song, et al. Recursive least squares algorithm with adaptive forgetting factor base...

    • ...The robot model used in the following experiments is the simulated e-puck robot [14] extended with 8 infra-red sensors which can measure distances in the range [5-80] cm. We use the Webots simulation environment [12] for data generation and navigation experiments, providing physicallyrealistic simulations (the simulator detects collisions and simulates physical properties of objects, such as the mass, the velocity, the inertia, the ...
    • ...This generalization capability is expected to work with our proposed architecture once it has been shown that reservoir architectures can learn and generalize obstacle avoidance behaviors [24]...

    Eric A. Antonelo, et al. Supervised learning of internal models for autonomous goal-oriented ro...

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