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Robotics Related

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

Identification of Beneficial Morphological Computation on Soft Hands

Compliance in soft hands can be both beneficial and detrimental to functionality. Although recent work has shown the benefits of compliance to object and environment geometry, there is little work in identifying and avoiding the negative aspects of compliance while controlling soft hands. However, a planner or a feedback-controller that exploits compliance should avoid the regions of detrimental morphological computation and guide the interactions to the favorable ones. Luckily recent work in simulation has shown promising results in differentiating between beneficial/detrimental morphological computations. The challenge ahead is to whether these results can be transferred to real systems. Our lab's work in hand sensorization is a possible tool in this path. more to: Identification of Beneficial Morphological Computation on Soft Hands

Sensorized In-Hand Manipulation

The RBO Hand 2 is a highly compliant soft robotic hand. Its actuators passively adapt their shape to different objects and the environment. Even though the control of the pneumatic hand is relatively simple, it is capable of complex in-hand manipulation. The recent addition of liquid metal strain sensors has created the opportunity to obtain better feedback about the current state of the hand. The goal of this thesis is to utilize this new sensor information to make the execution of different in-hand manipulation tasks more robust. more to: Sensorized In-Hand Manipulation

Computational Biology Related

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