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TU Berlin

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Robotics Related

Analysis of Soft Finger Pulp Design on Grasping and Manipulation

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The goal of this thesis is to mitigate some of the issues the current air-mass control strategy of the soft-pneumatic-actuators introduces. So far the controller is derived in a data-driven way and does not rely on accurate sensor-based air-mass estimates. Therefore, when running the controller for a longer time duration, the internal air-mass estimates computed by the model are subject to drift. This results in not being able to control the actuators precisely anymore. In the recent past new air-flow-sensors have been developed that might be feasible for our requirements. more to: Analysis of Soft Finger Pulp Design on Grasping and Manipulation

Modelling and Understanding Cockatoo's Mechanical Problem Solving Behavior

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Robots should be able to examine and understand previously unseen kinematic structures, such as the furniture and devices in a kitchen. What are good strategies for a robot to explore its environment? Could we learn something from biological behavior to improve this aspect of our robots? In a collaboration with colleagues from Vienna, we try to understand how Cockatoos can learn to solve multi-step kinematic puzzles by building models of the birds' behavior. more to: Modelling and Understanding Cockatoo's Mechanical Problem Solving Behavior

A Fabric Based Tactile Sensor for Soft Robotics

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When we build soft robotic grippers, we draw inspiration from the compliance and softness of the human hand. But what makes the human hand one of the most powerful tools is it's tactile sense. Since the sensors that are established in rigid robotics an not applicable to soft actuators, we have to take a look at new materials and new fabrication methods. In this thesis, we want to present one possible way to introduce tactile sensing to a soft actuator without restricting its dexterous behavior, using the RBO Hand 3 actuator as an example. more to: A Fabric Based Tactile Sensor for Soft Robotics

Air-Mass Control of Soft Pneumatic Actuators with Air-Flow-Sensors

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The goal of this thesis is to mitigate some of the issues the current air-mass control strategy of the soft-pneumatic-actuators introduces. So far the controller is derived in a data-driven way and does not rely on accurate sensor-based air-mass estimates. Therefore, when running the controller for a longer time duration, the internal air-mass estimates computed by the model are subject to drift. This results in not being able to control the actuators precisely anymore. In the recent past new air-flow-sensors have been developed that might be feasible for our requirements. more to: Air-Mass Control of Soft Pneumatic Actuators with Air-Flow-Sensors

Computational Biology Related

Distograms in protein motion

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The thesis builds on the work from Ines Putz, applying the break through of distogram prediction from the contact prediction field to protein motion. Elastic Network Models are useful in determining the coarse-grained motion of proteins. During structural transitions, certain residue pairs that were spatially close become separated, so called breaking contacts. Incorporating this information improves the prediction of protein motion. In this thesis, we want to apply state of the art machine learning methods to breaking contact prediction and further evaluate distance prediction instead of binary contacts. Here, distograms can provide a way to vary the stiffness of the springs in the elastic network model to allow for more flexibility. more to: Distograms in protein motion

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