FIRST-MM · Flexible Skill Acquisition and Intuitive Robot Tasking for Mobile Manipulation in the Real World

The development of flexible mobile manipulation systems is a promising area for the robotics industry as it allows to combine the success of manipulation robots with the flexibility of mobile robots. In the past, there has been a tremendous success in the areas of robotic manipulators and mobile robots. Many industrial processes highly depend on the reliability and robustness of robotic manipulators. On the other hand, research on mobile robots has led to systems that demonstrated the capability of safe and accurate navigation. The goal of this project is to integrate these two areas in the context of a real-world application scenario to build the basis for a new generation of autonomous mobile manipulation robots that can flexibly be instructed to perform complex manipulation and transportation tasks. The project will develop a novel robot programming environment that allows even non-expert users to specify complex manipulation tasks in real-world environments. In addition to a task specification language, the environment includes concepts for probabilistic inference and for learning manipulation skills from demonstration and from experience. The project will build upon and extend recent results in robot programming, navigation, manipulation, perception, learning by instruction, and statistical relational learning to develop advanced technology for mobile manipulation robots that can flexibly be instructed even by non-expert users to perform challenging manipulation tasks in real-world environments.The project results will be evaluated in a real-world setting involving mobile manipulation platforms built from state-of-the-art components and controlled by a fully integrated software system containing all developed components. The integrated system will, starting from a task specification, be capable of acquiring necessary low-level manipulation skills, imitating mobile manipulation behaviors demonstrated by a human it interacts with and, most importantly, will be able to generalize over such demonstrated behaviors to autonomously solve other tasks.