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TU Berlin

Inhalt des Dokuments

Robotics Related

Predicting Interaction Points for Robot Exploration from RGB-D Data

14. August 2017

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A large part of our everyday environment consist of rigid bodies and kinematic joints that connect them. Together these joints and bodies form kinematic structures, such as cupboards, drawers, doors etc. and robots that work in our homes or offices need to be able to explore and analyze such kinematic structures. In order to reveal and learn about kinematic joints, robots first need to make the objects attached to these joints move. E.g. in order to estimate the direction in which a door opens, a robot needs to first grasp the handle of that door and then pull or push it. Only as soon as it starts to move, the robot can estimate where the rotational axis of that door is. Before the robot can actually grasp and excert forces onto an object, it needs to create hypotheses where and how to grasp. The focus of this thesis would be on generating such hypotheses. more to: Predicting Interaction Points for Robot Exploration from RGB-D Data

Predicting Kinematic Degrees of Freedom from RGB-D Data

14. August 2017

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A large part of our everyday environment consist of rigid bodies and kinematic joints that connect them. Together these joints and bodies form kinematic structures, such as cupboards, drawers, doors etc. and robots that work in our homes or offices need to be able to explore and analyze such kinematic structures. An important aspect of this exploration is the generation of initial hypotheses about the location and parameters of kinematic joints, based on visual input. If a robot can generate a good set of such initial hypotheses, this can guide its exploration and limit the number of actions that it needs to reveal kinematic degrees of freedom. In this thesis you develop a method for predicting kinematic degrees of freedom from RGB-D data. more to: Predicting Kinematic Degrees of Freedom from RGB-D Data

Identification of Beneficial Morphological Computation on Soft Hands

02. May 2017

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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

Motion Planning Under Uncertainty for Soft Manipulation

20. April 2017

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Uncertainty is the major obstacle for robots manipulating objects in the real world. A robot can never perfectly know its position in the world, the position of objects, and the outcome of its actions. A particularly hard challenge is motion planning under uncertainty. How should the robot move, if the model of the world might be wrong or incomplete? However, a robot can significantly reduce uncertainty if it uses contact sensing to establish controlled contact with the environment. Imagine a robot pushing objects into an edge of the environment - this action will reduce uncertainty over all objects positions. more to: Motion Planning Under Uncertainty for Soft Manipulation

Sensorized In-Hand Manipulation

08. May 2017

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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

Leveraging Data from Mass Spectrometry to Improve Cross-Links

02. May 2017

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Cross-Links are experimentally derived distance constraints that can be used to guide protein structure prediction. The current cross-linking data is obtained from multiple mass spectrometer runs and pre-filtered. In the pre-fltering step we lose a lot of information. For instance, there is ambiguity in the data that is currently handled in a greedy fashion. The idea of this project is to go one step back and look at the "raw" data from the mass spectrometry experiments. The hope is by including external information and dealing directly with the ambiguity in the data we can improve the number of cross-links and/or need fewer experiments for a similar yield. more to: Leveraging Data from Mass Spectrometry to Improve Cross-Links

Leveraging Crosslinks for Template Retrieval

15. May 2017

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Cross-Link Mass Spectometry is an experimental technique that provides residue distance constraints on the native structure. The method has been previously used to guide ab initio protein structure prediction. You will develop in this thesis a method that leverages cross-links to find homologous structures to the target in the PDB. For that, you will compare the distance constraints provided by cross-links with simulated cross-links for the templates from the PDB. more to: Leveraging Crosslinks for Template Retrieval

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