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TU Berlin

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Robotics Related

Active Acoustic Sensing

Sensors are an important part of any robot control system. While soft pneumatic actuators can't use most sensors from rigid robotics, they exhibit properties that make new sensing modalities possible. The air inside the airchamber of a pneumatic actuator conducts sound and this sound carries information about where it originated and which path it traveled. This thesis develops and characterizes a contact sensor by playing back a known sound and analyzing how it changes when a PneuFlex actuator touches an object in different ways. more to: Active Acoustic Sensing

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. more to: Recurrent State Representation Learning with Robotic Priors

Computational Biology Related

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