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Cogito: Plan-based Control of Robotic Agents

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.

Project details

We investigate the plan-based control of autonomous mobile robots performing everyday pick-and-place tasks in human environments. Our approach applies AI planning techniques to transform default plans that can be inferred from instructions into flexible, high-performance robot plans. To find high performance plans the planning system applies transformations such as carrying plates to the table by stacking them or leaving cabinet doors open while setting the table, which require substantial changes of the control structure of the intended activities.

Cognition requires technical systems to reason about and revise their own control programs. In particular, the systems must be capable of predicting the effects of their intended courses of action, learning routine controllers from experience, including advice into the behaviour specifications, and explaining their own behaviour. Having such capabilities for complex and changing activities requires technical systems to form, maintain, and execute plans - control programs that cannot only be executed but also reasoned about, generated, and revised during their execution. Indeed, it is almost impossible to imagine that cognitive technical systems performing non-trivial, dynamically changing, and possibly interfering tasks could be successful without performing plan-based control.

The Cogito project builds on Structured Reactive Controllers (SRCs), one of the leading-edge plan-based robot control systems for autonomous service robots developed by our research group. Cogito develops the next generation of plan-based controllers that differs from the current generation in that it provides built-in mechanisms for all cognitive capabilities listed above.

Key contributions to the research area of plan-based control of robotic agents is the application of transformational planning and learning to concurrent reactive manipulation tasks. In this approach transformational planning cannot only be used to find plans faster but also to improve the flexibility, reliability, and performance of plans.

We have shown in experiments that using the Cogito approach we can specify reliable robot plans for everyday activity that are general, flexible, and efficient. Indeed we have shown that our plans can recover from 86% of local failures with the remaining failures being ones such as an object slipping out of the gripper and falling to an unreachable position. Improving the plans by changing the problem-solving strategy requires the planner to reason through plans that are on average several thousands lines of code. We have demonstrated that in spite of this complexity transformational planning and learning can improve the plan performance by 23% to 43% depending on the tasks using very general plan transformation rules.

Acknowledgements

This project was partly funded by CoTeSys.

Publications

Journal Articles and Book Chapters

Towards Performing Everyday Manipulation Activities (Michael Beetz, Dominik Jain, Lorenz Mösenlechner, Moritz Tenorth), In Robotics and Autonomous Systems, Elsevier, volume 58, 2010. [bib] [pdf]
Learning from Humans -- Cognition-enabled Computational Models of Everyday Activity (Michael Beetz, Martin Buss, Bernd Radig), In Künstliche Intelligenz, Springer, 2010. [bib]
Robot Learning Language -- Integrating Programming and Learning for Cognitive Systems (Alexandra Kirsch), In Robotics and Autonomous Systems Journal, volume 57, 2009. [bib] [pdf]

Conference Papers

Movement-aware Action Control -- Integrating Symbolic and Control-theoretic Action Execution (Ingo Kresse, Michael Beetz), In IEEE International Conference on Robotics and Automation (ICRA), 2012. [bib] [pdf]
Robots that Validate Learned Perceptual Models (Ulrich Klank, Lorenz Mösenlechner, Alexis Maldonado, Michael Beetz), In IEEE International Conference on Robotics and Automation (ICRA), 2012. [bib] [pdf]
CRAM -- a Cognitive Robot Abstract Machine (Michael Beetz, Lorenz Mösenlechner, Moritz Tenorth, Thomas Rühr), In 5th International Conference on Cognitive Systems (CogSys 2012), 2012. [bib] [pdf]
Robotic Roommates Making Pancakes (Michael Beetz, Ulrich Klank, Ingo Kresse, Alexis Maldonado, Lorenz Mösenlechner, Dejan Pangercic, Thomas Rühr, Moritz Tenorth), In 11th IEEE-RAS International Conference on Humanoid Robots, 2011. [bib] [pdf]
Parameterizing Actions to have the Appropriate Effects (Lorenz Mösenlechner, Michael Beetz), In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011. [bib] [pdf]
Becoming Action-aware through Reasoning about Logged Plan Execution Traces (Lorenz Mösenlechner, Nikolaus Demmel, Michael Beetz), In IEEE/RSJ International Conference on Intelligent RObots and Systems., 2010. [bib]
CRAM -- A Cognitive Robot Abstract Machine for Everyday Manipulation in Human Environments (Michael Beetz, Lorenz Mösenlechner, Moritz Tenorth), In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010. [bib] [pdf]
Using Physics- and Sensor-based Simulation for High-fidelity Temporal Projection of Realistic Robot Behavior (Lorenz Mösenlechner, Michael Beetz), In 19th International Conference on Automated Planning and Scheduling (ICAPS'09)., 2009. [bib] [pdf]
Transformational Planning for Mobile Manipulation based on Action-related Places (Andreas Fedrizzi, Lorenz Moesenlechner, Freek Stulp, Michael Beetz), In Proceedings of the International Conference on Advanced Robotics (ICAR)., 2009. [bib] [pdf]
The Assistive Kitchen -- A Demonstration Scenario for Cognitive Technical Systems (Michael Beetz, Freek Stulp, Bernd Radig, Jan Bandouch, Nico Blodow, Mihai Dolha, Andreas Fedrizzi, Dominik Jain, Uli Klank, Ingo Kresse, Alexis Maldonado, Zoltan Marton, Lorenz Mösenlechner, Federico Ruiz, Radu Bogdan Rusu, Moritz Tenorth), In IEEE 17th International Symposium on Robot and Human Interactive Communication (RO-MAN), Muenchen, Germany, 2008. (Invited paper.) [bib] [pdf]
Transformational Planning for Everyday Activity (Armin Müller, Alexandra Kirsch, Michael Beetz), In Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS'07), 2007. [bib] [pdf]
Training on the Job --- Collecting Experience with Hierarchical Hybrid Automata (Alexandra Kirsch, Michael Beetz), In Proceedings of the 30th German Conference on Artificial Intelligence (KI-2007) (J. Hertzberg, M. Beetz, R. Englert, eds.), 2007. [bib] [pdf]
Towards High-performance Robot Plans with Grounded Action Models: Integrating Learning Mechanisms into Robot Control Languages (Alexandra Kirsch), In ICAPS Doctoral Consortium, 2005. [bib] [pdf]
Combining Learning and Programming for High-Performance Robot Controllers (Alexandra Kirsch, Michael Beetz), In Tagungsband Autonome Mobile Systeme 2005, Springer Verlag, 2005. [bib] [pdf]
Object-oriented Model-based Extensions of Robot Control Languages (Armin Müller, Alexandra Kirsch, Michael Beetz), In 27th German Conference on Artificial Intelligence, 2004. [bib] [pdf]
RPL-LEARN: Extending an Autonomous Robot Control Language to Perform Experience-based Learning (Michael Beetz, Alexandra Kirsch, Armin Müller), In 3rd International Joint Conference on Autonomous Agents & Multi Agent Systems (AAMAS), 2004. [bib] [pdf]

Workshop Papers

High Performance Execution of Everyday Pick-and-Place Tasks by Integrating Transformation Planning and Reactive Execution (Lorenz Mösenlechner, Armin Müller, Michael Beetz), In Proceedings of the 1st International Workshop on Cognition for Technical Systems, München, Germany, 6-8 October, 2008. [bib]
Towards a Plan Library for Household Robots (Armin Müller, Michael Beetz), In Proceedings of the ICAPS'07 Workshop on Planning and Plan Execution for Real-World Systems: Principles and Practices for Planning in Execution, 2007. [bib] [pdf]
The Assistive Kitchen --- A Demonstration Scenario for Cognitive Technical Systems (Michael Beetz, Jan Bandouch, Alexandra Kirsch, Alexis Maldonado, Armin Müller, Radu Bogdan Rusu), In Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (HAM), 2007. [bib] [pdf]
Making Robot Learning Controllable: A Case Study in Robot Navigation (Alexandra Kirsch, Michael Schweitzer, Michael Beetz), In Proceedings of the ICAPS Workshop on Plan Execution: A Reality Check, 2005. [bib] [pdf]

Other Publications

Transformational Planning for Autonomous Household Robots using Libraries of Robust and Flexible Plans (Armin Müller), PhD thesis, Technische Universität München, 2008. [bib] [pdf]
Integration of Programming and Learning in a Control Language for Autonomous Robots Performing Everyday Activities (Alexandra Kirsch), PhD thesis, Technische Universität München, 2008. [bib] [pdf]
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Last edited 01.08.2011 14:49 by tenorth