Research

Currently I am working on Action-Related Places, which propose base positions from where robots can successfully perform a subsequent manipulation action. The reason why this research question is important is because in mobile manipulation, the navigation and manipulation are tightly coupled. The base position from where the robot performs a manipulation action has a large impact on being successful or not. Action-Related Places are a utility-based approach that uses experience-based learning in order to find the most promising base positions. Some properties worth mentioning are:

• ARPlaces are grounded in experience, and thus tailored towards a robot's skills.
• Instead of committing to a specific position in advance, ARPlace gets updated as new sensor data comes in. This enables least commitment planning.
• Using ARPlaces to select base positions for manipulation is more robust towards state estimation uncertainties than previous approaches.
• ARPlaces can be merged in order to grasp multiple objects from a single base pose.

 More information about Action-Related Places can be obtained on the corresponding project page here.

Research Topics

• Learning Grounded Models

• Automated Learning

• Plan-Based Robot Control

• Human and Robot Manipulation

Current Research Projects

• CogMan : The CogMan project (1) develops computational and control models of pick-and-place tasks in the context of everyday manipulation activities in human environments, (2) implements the model into a control system for the kitchen scenario, and (3) empirically analyzes the impact of this control model on the flexibility, robustness, adaptability, and naturality of the robot behavior.

• Cogito : A key challenge for the next generation of autonomous robots is the reliable and efficient accomplishment of prolonged, complex, and dynamically changing tasks in the real world. One of the most promising approaches to realizing these capabilities is the plan-based approach to robot control. In the plan-based approach, robots produce control actions by generating, maintaining, and executing plans that are tailored for the robots' respective tasks. Plans are robot control programs that a robot can not only execute but also reason about and manipulate. Thus a plan-based controller is able to manage and adapt the robot's intended course of action --- the plan --- while executing it and can thereby better achieve complex and changing goals. The use of plans enables these robots to flexibly interleave complex and interacting tasks, exploit opportunities, quickly plan their courses of action, and, if necessary, revise their intended activities. One of the grand visions in the area of plan-based robot control is the realization of general autonomous robot control programs that can adapt themselves to the environments they are to operate in and to the distribution of complex tasks they are to perform. An instance of this grand vision is a pre-programmed household robot that knows how to clean a kitchen, how to operate a dishwasher, and so on. Being installed in a new environment it specializes its general plans to the specifics of the household and learns to manage the specific agenda of household chorus that is given to it. The robot also has to learn about the pitfalls of its tasks and its environment and avoid them through foresight. Our research field is still far away from realizing such competent robotic agents.

Teaching Inf. IX (Current Term)

Teaching General

Selected Publications

• Andreas Fedrizzi , Lorenz Mösenlechner , Freek Stulp , Michael Beetz , Transformational Planning for Mobile Manipulation based on Action-related Places, 2009, Proceedings of the International Conference on Advanced Robotics (ICAR).,

  (BibTeX) (PDF)

• Michael Beetz , Freek Stulp , Piotr Esden-Tempski , Andreas Fedrizzi , Ulrich Klank , Ingo Kresse , Alexis Maldonado , Federico Ruiz-Ugalde , Generality and Legibility in Mobile Manipulation, 2010, Autonomous Robots Journal (Special Issue on Mobile Manipulation),

  (BibTeX) (PDF)

• Freek Stulp , Andreas Fedrizzi , Michael Beetz , Action-Related Place-Based Mobile Manipulation, 2009, Proceedings of the International Conference on Intelligent Robots and Systems (IROS),

  (BibTeX) (PDF)

• Freek Stulp , Andreas Fedrizzi , Franziska Zacharias, Moritz Tenorth , Jan Bandouch , Michael Beetz , Combining Analysis, Imitation, and Experience-based Learning to Acquire a Concept of Reachability, 2009, 9th IEEE-RAS International Conference on Humanoid Robots,

  (BibTeX) (PDF)

• Freek Stulp , Andreas Fedrizzi , Michael Beetz , Learning and Performing Place-based Mobile Manipulation, 2009, Proceedings of the 8th International Conference on Development and Learning (ICDL).,

  (BibTeX) (PDF)