Our paper, resulting from work of Master student Gustavo Claudio Karl Couto, entitled
"Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments" has shown very interesting results:
Both the intermediate mid-level input BEV representation and the control policy are learned as the agent navigates in an urban town (click for more videos from playlist).
State-of-the-art Artificial Intelligence method for detecting that you is really you and not some intruder entering the code on your mobile phone.
This work proposes a general Reservoir Computing (RC) learning framework which can be used to learn navigation behaviors for mobile robots in simple and complex unknown, partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) fixed while only a linear readout output layer is trained.
The proposed RC framework builds upon the notion of navigation attractor or behavior which can be embedded in the high-dimensional space of the reservoir after learning.
The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir.
Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using an hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors towards the goal.
