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Robotics and Biology LaboratoryOpen Theses

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Robotics Related

Environment Design in Motion Planning

13. April 2018


Exploiting the environment is useful to reduce robot state uncertainty due to very reliable force feedback. Therefore, we extended an RRT based motion planner with motion actions which allow the planner to make contact with the environment. This raised the importance of actual shape and availability of environment features to enable contacts. A first environment design algorithm has been developed that iteratively adds new environment features to a given scene. To do so, the algorithm detects and models areas of high uncertainty to then constructs and places landmarks into the uncertain areas. more to: Environment Design in Motion Planning

Life-long grasp success learning

25. July 2018


Grasping is a hard task for robots, because it is impossible to model the world and the robot dynamics perfectly. Without exhaustive modeling, we can increase the grasp performance of the robot by exploiting features of the environment like the wall of a crate. This features are called environment constraints (EC). The robot can push the object to one of the walls, by which the object motion is constrained along the wall and its location uncertainty is also reduced. more to: Life-long grasp success learning

Motion Planning Under Uncertainty for Soft Manipulation

20. April 2017


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

Computational Biology Related

Leveraging Data from Mass Spectrometry to Improve Cross-Links


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


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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