Now that the 11th year of Google Summer of Code has officially come to a close, we will devote Fridays to wrap-up posts from a handful of the 137 mentoring organizations that participated in 2015. Organizations this year represented a wide range of computing fields including artificial intelligence, featured below.
Two software libraries that originate from our laboratory, the Institute for Artificial Intelligence, that are used and supported by a larger user community are the KnowRob system for robot knowledge processing and the CRAM (Cognitive Robot Abstract Machine) framework for plan-based robot control. In our group, we have a very strong focus on open source software and active maintenance and integration of projects. The systems we develop are available under BSD and MIT licenses, and partly (L)GPL.
Within the context of these frameworks, we offered four projects during the summer term in 2015, which were all accepted to Google Summer of Code (GSoC).
Multi-modal Big Data Analysis for Robotic Everyday Manipulation Activities
Within the context of these frameworks, we offered four projects during the summer term in 2015, which were all accepted to Google Summer of Code (GSoC).
Multi-modal Big Data Analysis for Robotic Everyday Manipulation Activities
The project "Multi-modal Big Data Analysis for Robotic Everyday Manipulation Activities" added to our ongoing work to build the robotic perception system RoboSherlock for service robots performing household chores. Our GSoC student, Alexander, made exciting progress and valuable contributions during the summer. He ported an earlier prototypical proprioceptive module from Java to C++ to integrate it into RoboSherlock, he developed tools for visualizing the module's various detections and annotations, and applied this infrastructure to detect collisions of the robot's arms with unperceived parts of the environment in a shelf reordering task. We are also very happy that Alexander decided to stay and keep on working on RoboSherlock after GSoC ended.
Kitchen Activity Games GUI
Our GSoC student, Mesut, developed a GUI to interact with the robotics simulator Gazebo. The simulator has been used as a library, allowing different scenarios (worlds) to be selected and executed. Playlists can be generated in order to replay logged episodes. During the replay, various plugins can be linked and executed from the GUI to allow post processing the data. The user interface will ease organizing and saving simulation data further used for learning. You can view Mesut’s project on GitHub here.
Symbolic Reasoning Tools with Bullet using CRAM
Autonomous robots performing complex manipulation tasks in household environments, such as preparing a meal or tidying up, are required to know where different objects are located and what properties they have. The knowledge about their environment is called “belief state”, i.e. the information that the robot believes holds true in the surrounding world. Our GSoC student, Kunal, worked on improving the world representation of the CRAM robotic framework, which represents the environment as a 3-dimensional world where simple physics rules of the Bullet Physics engine apply. The goal of the project was to issue events when errors are found in the belief state, such as, if the robot thinks its arm is inside of a table, which is physically impossible. A stand-alone ROS (Robot Operating System) publisher node, that would notify all its listeners about errors, was partially implemented while integration with the CRAM belief state is still in progress.
Report Card Generation from Robot Mobile Manipulation Activities
Throughout the summer, our GSoC student Kacper made great progress in developing a framework for automatically generating report cards from robot experiences. We have a special focus in mobile manipulation activities in robots and are interested in anomaly detection in our rather complex systems — the developed components greatly help us save time on mundane analysis tasks, and make complicated analysis steps (looking up all aspects of a certain action, comparing different trials) easier to do.
By Jan Winkler, Organization Administrator and PhD student at the Institute of Artificial Intelligence
