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Planning Everyday Manipulation Tasks

  • Daniel Sebastian Leidner
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 127)

Abstract

This chapter constitutes the second of the four main chapters in the concept of Intelligent Physical Compliance. It addresses the question on how symbolic task descriptions can be translated into meaningful robot operations in the context of wiping tasks. To achieve this, three main elements are investigated, namely, symbol grounding and planning with symbols, planning algorithms for mobile manipulation, and effect-space planning for wiping tasks.

References

  1. Bejjani, Wissam. 2015. Automated Planning of Whole-body Motions for Everyday Household Chores with a Humanoid Service Robot. Master’s thesis, Technical University of Dortmund.Google Scholar
  2. Buss, Samuel R. 2004. Introduction to Inverse Kinematics with Jacobian Transpose, Pseudoinverse and Damped Least Squares Methods. IEEE Journal of Robotics and Automation 17 (1–19): 16.Google Scholar
  3. Christensen, Henrik I, et al. 2013. A Roadmap for U.S. Robotics from Internet to Robotics. Technical report, Robotics Virtual Organization.Google Scholar
  4. Cakmak, Maya and Leila Takayama. 2013. Towards a Comprehensive Chore List for Domestic Robots. In Proceedings of the ACM/IEEE International Conference on Human-robot Interaction (HRI), pp. 93–94.Google Scholar
  5. Diankov, Rosen. 2010. Automated Construction of Robotic Manipulation Programs. PhD thesis, Carnegie Mellon University, Robotics Institute.Google Scholar
  6. Donald, A. 1980. Norman and Tim Shallice. Attention to Action: Willed and Automatic Control of Behavior. Technical report, DTIC Document.Google Scholar
  7. Dornhege, Christian, Patrick Eyerich, Thomas Keller, Sebastian Trüg, Michael Brenner, and Bernhard Nebel. 2012. Semantic Attachments for Domain-independent Planning Systems. In Towards Service Robots for Everyday Environments, pp. 99–115. Springer.Google Scholar
  8. Graham, Ronald L., and Pavol Hell. 1985. On the hIstory of The Minimum Spanning Tree Problem. Annals of the History of Computing 7 (1): 43–57.MathSciNetCrossRefGoogle Scholar
  9. Ghallab, Malik, Adele Howe, Dave Christianson, Drew McDermott, Ashwin Ram, Manuela Veloso, Daniel Weld, and David Wilkins. 1998. PDDL—The Planning Domain Definition Language. AIPS98 Planning Committee 78 (4): 1–27.Google Scholar
  10. Galindo, Cipriano, Juan-Antonio Fernández-Madrigal, Javier González, and Alessandro Saffiotti. 2008. Robot Task Planning using Semantic Maps. Robotics and Autonomous Systems 56 (11): 955–966.CrossRefGoogle Scholar
  11. Gabriely, Yoav, and Elon Rimon. 2001. Spanning-Tree based Coverage of Continuous Areas by a Mobile Robot. Annals of Mathematics and Artificial Intelligence 31 (1–4): 77–98.CrossRefGoogle Scholar
  12. Harnad, Stevan. 1990. ThE Symbol Grounding Problem. Physica D: Nonlinear Phenomena 42 (1–3): 335–346.CrossRefGoogle Scholar
  13. Helmert, Malte. 2006. The Fast Downward Planning System. Journal of Artifcial Intelligence Research 26: 191–246.CrossRefGoogle Scholar
  14. Hart, Peter E., Nils J. Nilsson, and Bertram Raphael. 1968. A Formal Basis for The Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics 4 (2): 100–107.CrossRefGoogle Scholar
  15. Hayes, Patrick J. 1978. The Naive Physics Manifesto. Institut pour les études sémantiques et cognitives/Université de Genève.Google Scholar
  16. Hess, Jürgen Michael, Gian Diego Tipaldi, and Wolfram Burgard. 2012. Null Space Optimization for Effective Coverage of 3D Surfaces using Redundant Manipulators. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1923–1928.Google Scholar
  17. Huaman, Ana and Mike Stilman. 2012. Deterministic Motion Planning for Redundant Robots Along End-effector Paths. In Proceedings of the International Conference on Humanoid Robots (ICHR), pp. 785–790.Google Scholar
  18. Konietschke, Rainer and Gerd Hirzinger. 2009. Inverse Kinematics with Closed form Solutions for Highly Redundant Robotic Systems. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2945–2950. IEEE.Google Scholar
  19. Karlsson, Lars, Julien Bidot, Alessandro Saffiotti, Ulrich Hillenbrand, and Florian Schmidt. 2012. Combining Task and Path Planning for a Humanoid Two-arm Robotic System. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pp. 13–20.Google Scholar
  20. Kaelbling, Leslie Pack and Tomás Lozano-Pérez. 2011. Hierarchical Task and Motion Planning in the Now. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1470–1477.Google Scholar
  21. Leidner, Daniel, Christoph Borst, and Gerd Hirzinger. 2012. Things are Made for What They Are: Solving Manipulation Tasks by Using Functional Object Classes. In Proceedings of the IEEE/RAS International Conference on Humanoid Robots (ICHR), pp. 429–435.Google Scholar
  22. Leidner, Daniel, Alexander Dietrich, Michael Beetz, and Alin Albu-Schäffer. 2016b. Knowledge-enabled Parameterization of Whole-body Control Strategies for Compliant Service Robots. Autonomous Robots (AURO): Special Issue on Whole-Body Control of Contacts and Dynamics for Humanoid Robots 40 (3): 519–536.Google Scholar
  23. Leidner, Daniel and Christoph Borst. 2013. Hybrid reasoning for Mobile Manipulation based on Object Knowledge. In Proceedings of the Workshop on AI-Based Robotics at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
  24. Leidner, Daniel, Alexander Dietrich, Florian Schmidt, Christoph Borst, and Alin Albu-Schäffer. 2014b. Object-centered Hybrid Reasoning for Whole-body Mobile Manipulation. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1828–1835.Google Scholar
  25. Leidner, Daniel, Wissam Bejjani, Alin Albu-Schäffer, and Michael Beetz. 2016a. Robotic Agents Representing, Reasoning, and Executing Wiping Tasks for Daily Household Chores. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1006–1014.Google Scholar
  26. Liepert, Bernd, Stefano Stramigioli, Rainer Bischoff, Uwe Haass, et al. 2014. Multi-annual Roadmap for Robotics in Europe. Technical report, euRobotics Association Internationale Sans But Lucratif (AISBL).Google Scholar
  27. LaValle, Steven M. 1998. Rapidly-Exploring Random Trees: A New Tool for Path Planning.Google Scholar
  28. Latombe, Jean-Claude. 1990. Robot Motion Planning. Springer.Google Scholar
  29. Nüchter, Andreas, and Joachim Hertzberg. 2008. Towards Semantic Maps for Mobile Robots. Robotics and Autonomous Systems 56 (11): 915–926.CrossRefGoogle Scholar
  30. Okada, Kei, Takashi Ogura, Atsushi Haneda, Junya Fujimoto, Fabien Gravot, and Masayuki Inaba. 2005. Humanoid Motion Generation System on HRP2-JSK for Daily Life Environment. In Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1772–1777.Google Scholar
  31. Okada, Kei, Mitsuharu Kojima, Yuuichi Sagawa, Toshiyuki Ichino, Kenji Sato, and Masayuki Inaba. 2006. Vision Based Behavior Verification System of Humanoid Robot for Daily Environment Tasks. In Proceedings of the IEEE-RAS International Conference on Humanoid Robots (ICHR), pp. 7–12.Google Scholar
  32. Searle, John. 2001. The Chinese Room Argument. Encyclopedia of Cognitive Science.Google Scholar
  33. Schulman, John, Alex Lee, Ibrahim Awwal, Henry Bradlow, and Pieter Abbeel. 2013. Finding Locally Optimal, Collision-free Trajectories with Sequential Convex Optimization. In Proceedings of the Robotics: Science and Systems Conference (RSS)Google Scholar
  34. Stulp, Freek, Andreas Fedrizzi, Lorenz Mösenlechner, and Michael Beetz. 2012a. Learning and Reasoning with Action-related Places for Robust Mobile Manipulation. Journal of Artificial Intelligence Research 43: 1–42.MathSciNetCrossRefGoogle Scholar
  35. Urbanek, Holger, Alin Albu-Schäffer, and Patrick van der Smagt. 2004. Learning from Demonstration: Repetitive Movements for Autonomous Service Robotics. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3495–3500.Google Scholar
  36. Vahrenkamp, Nikolaus, Tamim Asfour, and Ruediger Dillmann. 2013. Robot Placement Based on Reachability Inversion. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1962–1967.Google Scholar
  37. Wolfe, Jason, Bhaskara Marthi, and Stuart J Russell. 2010. Combined Task and Motion Planning for Mobile Manipulation. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pp. 254–258.Google Scholar
  38. Zacharias, Franziska, Christoph Borst, and Gerd Hirzinger. 2007. Capturing Robot Workspace Structure: Representing Robot Capabilities. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3229–3236.Google Scholar
  39. Zacharias, Franziska, Wolfgang Sepp, Christoph Borst, and Gerd Hirzinger. 2009. Using a Model of the Reachable Workspace to Position Mobile Manipulators for 3-D Trajectories. In Proceedings of the IEEE/RAS International Conference on Humanoid Robots (ICHR), pp. 55–61.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Robotics and MechatronicsGerman Aerospace Center (DLR)WesslingGermany

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