Differences
This shows you the differences between two versions of the page.
| Next revisionBoth sides next revision |
| blog:openease [2015/06/01 09:50] – * mareikep | blog:openease [2015/06/01 09:51] – mareikep |
|---|
| |
| === Building knowledge on previous research === | === Building knowledge on previous research === |
| <html><style="float:right; margin-left:10px;"iv ><iframe width="320" height="215" src="//www.youtube.com/embed/HCguH-5xjTk" frameborder="0" allowfullscreen></iframe></div></html> | <html><div style="float:right; margin-left:10px;"><iframe width="320" height="215" src="//www.youtube.com/embed/HCguH-5xjTk" frameborder="0" allowfullscreen></iframe></div></html> |
| The idea behind [[http://www.open-ease.org|Open-EASE]] is that researchers worldwide focus on many specific problems to enable robots to work autonomously, but in order to perform complex tasks like preparing a meal, this knowledge has to be shared and the machines’ capabilities need to be combined. | The idea behind [[http://www.open-ease.org|Open-EASE]] is that researchers worldwide focus on many specific problems to enable robots to work autonomously, but in order to perform complex tasks like preparing a meal, this knowledge has to be shared and the machines’ capabilities need to be combined. |
| “Big data” storage, knowledge processing, cloud computing, and web technology are utilized to provide researchers with a database that allows an easy analysis of robot experiences, whether it’s their own or other scientists’ research. In the future, robotic systems will be able to access their own and other robot’s episodic memories directly as well. At the same time, the data from all experiments can be used in new projects and in teaching. All information is gathered automatically while a robot performs a task. The collected data includes positions of individual parts, images from the machine’s perception system, and other sensor and control signal streams. In the database all information is annotated with semantic indexing structures that are automatically generated by the interpreter of the robot control system. These episodic memories can help the robotic systems assess what they were doing and enable them to answer queries about what they did, why they did it, how they did it, what they saw when they did it, and what happened when they did it. A web-based graphical query interface reduces the time and effort for such analyses dramatically. | “Big data” storage, knowledge processing, cloud computing, and web technology are utilized to provide researchers with a database that allows an easy analysis of robot experiences, whether it’s their own or other scientists’ research. In the future, robotic systems will be able to access their own and other robot’s episodic memories directly as well. At the same time, the data from all experiments can be used in new projects and in teaching. All information is gathered automatically while a robot performs a task. The collected data includes positions of individual parts, images from the machine’s perception system, and other sensor and control signal streams. In the database all information is annotated with semantic indexing structures that are automatically generated by the interpreter of the robot control system. These episodic memories can help the robotic systems assess what they were doing and enable them to answer queries about what they did, why they did it, how they did it, what they saw when they did it, and what happened when they did it. A web-based graphical query interface reduces the time and effort for such analyses dramatically. |
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
Discover our VRB for innovative and interactive research
Memberships and associations:
Social Media:
