TU Berlin

Robotics and Biology LaboratoryManuel Baum

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Manuel Baum


Room: MAR 5.065
Telephone: +49.30.314-73 119
Fax: +4930 314-21116
Office hours: On request

Research interests

I am interested in interactive perception and task-directed exploration, two related and deeply robotic problems.

Interactive perception is important, as not all information that is relevant to an agent is readily available just from looking at the world. The agent needs to exert forces, interact with the world to reveal what's relevant, e.g. the weight of an object, or the degrees of freedom of a kinematic structure. Furthermore, as the agent knows which actions it performed to generate sensor data, it can make use of that information to interpret its input.

The world is complex, but robots are usually employed to solve a set of tasks for which they just need to know about a subset of the world. This is why it is important not to explore the environment randomly, but to perform task-directed exploration. But how to find out which information is actually relevant to a task? And how can we gather that information? I aim to answer these questions in my research.


Achieving Robustness in a Drawer Manipulation Task by using High-level Feedback instead of Planning
Citation key baum21DGR
Author Manuel Baum and Oliver Brock
Title of Book Proceedings of the DGR Days
Pages 29-29
Year 2021
Note http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen_pdf/baum21DGR.pdf
Abstract Robotic manipulation behavior should be robust to disturbances that violate high-level task-structure. Such robustness can be achieved by constantly monitoring the envi- ronment to observe the discrete high-level state of the task. This is possible because different phases of a task are characterized by different sensor patterns and by monitoring these patterns a robot can decide which controllers to execute. This eliminates the need to plan a temporal sequence of those controllers and makes the behavior robust to unforeseen disturbances. We implement this idea as a probabilistic filter over discrete states where each state directly activates a controller. Based on this framework we present a robotic system that is able to robustly open a drawer and grasp tennis balls from it.
Link to publication Link to original publication Download Bibtex entry


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