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EASE is a interdisciplinary research center at the University of Bremen that investigates everyday activity science & engineering. Everyday Activity Science and Engineering (EASE) is the study of the design, realization, and analysis of information processing models that enable robotic agents (and humans) to master complex human-scale manipulation tasks that are mundane and routine. EASE not only investigates action selection and control but also the methods needed to acquire the knowledge, skills, and competence required for flexible, reliable, and efficient mastery of these activities.

The key components, which are listed in the navigation panel are:

  • the fundamental research thread (Research) which organizes and executes the EASE research agenda
  • openEASE, which collects the community-based initiatives of EASE, including the openEASE knowledge service, the opensource software packages KNOWROB, ROBO SHERLOCK, ROBCOG, PRAC, CRAM, and GISKARD, open teaching and training efforts, as well as cooperative opportunities.
  • EASEacademy, which provides the EASE teaching and training facilities including the EASE doctoral training school, courses for students at the University of Bremen, offering for Highschool students, as well as internet teaching material
  • EASEinnovation, includes the technology transfer efforts of EASE, such as applied research and technology transfer projects, innovation activities
  • EASEoutreach provides information material for the general public and media

Publications

Jan Winkler, Asil Kaan Bozcuoglu, Mihai Pomarlan, Michael Beetz, Task Parametrization through Multi-modal Analysis of Robot Experiences (Extended Abstract), In Proceedings of the 2017 International Conference on Autonomous Agents, 2017. Accepted for publication [bib]

Simon Stelter, Georg Bartels, Michael Beetz, Multidimensional Time Series Shapelets Reliably Detect and Classify Contact Events in Force Measurements of Wiping Actions, In Robotics and Automation Letters, IEEE, 2017. Accepted for publication [bib]

Daniel Nyga, Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning, PhD thesis, University of Bremen, 2017. [bib] [pdf]

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