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Recurrent State Representation Learning with Robotic Priors


Learning an internal state based on the observation is an important task in robotics. The sensor inputs are mostly high dimensional and only a small subspace is important for the robot. Previous work presented an unsupervised method training a neural net with a loss composed of robotic priors which has been effective in a markovian observation space [1]. In this thesis, we try to extend this method to work on non markovian observation spaces, and train a recurrent network which should transfer the non markovian observations into a internal state space which fulfill the markov property. For this, we adapt the robotic prios to the new task and evaluate our method in a new experimental setting. As a goal, the robot should be able to solve a simple navigation task using only the learned state representation.

[1] Rico Jonschkowski and Oliver Brock. Learning State Representations with Robotic Priors. Autonomous Robots. Springer US 39(3):407-428, 2015.    

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