Advertisement

Journal of Intelligent & Robotic Systems

, Volume 80, Supplement 1, pp 189–203 | Cite as

A Case-Based Approach to Mobile Push-Manipulation

  • Tekin Meriçli
  • Manuela Veloso
  • H. Levent Akın
Article

Abstract

The complexity of the potential physical interactions between the robot, each of the pushable objects, and the environment is vast in realistic mobile push-manipulation scenarios. This makes it non-trivial to write generic analytical motion and interaction models or tune the parameters of physics engines for every robot, object, and environment combination. We present a case-based approach to push-manipulation that allows the robot to acquire, through interaction and observation, a set of discrete, experimental, probabilistic motion models (i.e. probabilistic cases) for pushable passively-mobile real world objects. These probabilistic cases are then used as building blocks for constructing achievable push plans to navigate the object of interest to the desired goal pose as well as to potentially push the movable obstacles out of the way in cluttered task environments. Additionally, incremental acquisition and updating of the probabilistic cases allows the robot to adapt to the changes in the environment, such as increased mass due to loading of the object of interest for transportation purposes. The purely interaction and observation driven nature of our method makes it robot, object, and environment (real or simulated) independent, as we demonstrate through validation tests in a real world setup in addition to extensive experimentation in simulation.

Keywords

Mobile push-manipulation Experience-based mobile manipulation Mobile manipulation learning Mobile manipulation planning Whole body manipulation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lynch, K. M.: Nonprehensile Robotic Manipulation: Controlability and Planning. PhD thesis, Robotics Institute Carnegie Mellon University (1996)Google Scholar
  2. 2.
    Rosenthal, S., Biswas, J., Veloso, M.: An Effective Personal Mobile Robot Agent Through Symbiotic Human-Robot Interaction. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2010)Google Scholar
  3. 3.
    Meriçli, T., Veloso, M., Akın, H.L.: Experience Guided Achievable Push Plan Generation for Passive Mobile Objects. In: Beyond Robot Grasping - Modern Approaches for Dynamic Manipulation, IROS’12, (2012)Google Scholar
  4. 4.
    Meriçli, T., Veloso, M., Akın, H.L.: Achievable Push-Manipulation for Complex Passive Mobile Objects using Past Experience. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2013)Google Scholar
  5. 5.
    Meriçli, T., Veloso, M., Akın, H.L.: Push-Manipulation of Complex Passive Mobile Objects using Experimentally Acquired Motion Models. Auton. Robot., 1–13 (2014)Google Scholar
  6. 6.
    Veloso, M.M.: Planning and Learning by Analogical Reasoning. Springer Verlag (1994)Google Scholar
  7. 7.
    Lynch, K.M., Mason, M.T.: Dynamic nonprehensile manipulation: Controllability, planning, and experiments. Int. J. Robot. Res. 18, 64–92 (1997)CrossRefGoogle Scholar
  8. 8.
    Khatib, O.: Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. Int. J. Robot. Res. 5(1), 90–98 (1986). SpringMathSciNetCrossRefGoogle Scholar
  9. 9.
    Igarashi, T., Kamiyama, Y., Inami, M.: A Dipole Field for Object Delivery by Pushing on a Flat Surface. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2010)Google Scholar
  10. 10.
    Lau, M., Mitani, J., Igarashi, T.: Automatic Learning of Pushing Strategy for Delivery of Irregular-Shaped Objects. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2011)Google Scholar
  11. 11.
    Walker, S., Salisbury, J.K.: Pushing Using Learned Manipulation Maps. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2008)Google Scholar
  12. 12.
    Zito, C., Stolkin, R., Kopicki, M., Wyatt, J.: Two-level RRT Planning for Robotic Push Manipulation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012)Google Scholar
  13. 13.
    Kopicki, M., Zurek, S., Stolkin, R., Mörwald, T., Wyatt, J.: Learning to predict how rigid objects behave under simple manipulation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2011)Google Scholar
  14. 14.
    Scholz, J., Stilman, M.: Combining motion planning and optimization for flexible robot manipulation. In: Humanoid Robots (Humanoids), In 2010 10th IEEE-RAS International Conference on, pp 80–85 (2010)Google Scholar
  15. 15.
    Stilman, M.: Navigation Among Movable Obstacles. PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh (2007)Google Scholar
  16. 16.
    Stilman, M., Kuffner, J.J.: Navigation Among Movable Obstacles: Real-time Reasoning in Complex Environments. Int. J. Humanoid Rob. 2(04), 479–503 (2005)CrossRefGoogle Scholar
  17. 17.
    Stilman, M., Nishiwaki, K., Kagami, S., Kuffner, J.: Planning and Executing Navigation Among Movable Obstacles. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 820–826 (2006)Google Scholar
  18. 18.
    Dogar, M., Srinivasa, S.: A Planning Framework for Non-Prehensile Manipulation under Clutter and Uncertainty. Autonomous Robots 33(3), 217–236 (2012)CrossRefGoogle Scholar
  19. 19.
    Uğur, E., Öztop, E., Şahin, E.: Goal emulation and planning in perceptual space using learned affordances. Robot. Auton. Syst. 59(7–8), 580–595 (2011)Google Scholar
  20. 20.
    Şahin, E., Çakmak, M., Doğar, M.R., Uğur, E., Üçoluk, G.: To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control. Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems 15(4), 447–472 (2007)Google Scholar
  21. 21.
    Wolpert, D.M., Kawato, M.: Multiple Paired Forward and Inverse Models for Motor Control. Neural Netw. 11(7-8), 1317–1329 (1998)CrossRefGoogle Scholar
  22. 22.
    Haruno, M., Wolpert, D.M., Kawato, M.M.: MOSAIC Model for Sensorimotor Learning and Control. Neural Comput. 13(10), 2201–2220 (2001)CrossRefzbMATHGoogle Scholar
  23. 23.
    Bartsch-Spörl, B., Lenz, M., Hübner, A.: Case-Based Reasoning - Survey and Future Directions. In: Proceedings of the 5th German Biennial Conference on Knowledge-Based Systems, pp 67–89. Springer Verlag (1999)Google Scholar
  24. 24.
    Spalazzi, L.: A Survey on Case-Based Planning. Artif. Intell. Rev. 16, 3–36 (2001)CrossRefzbMATHGoogle Scholar
  25. 25.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. Acoustics, Speech and Signal Processing, IEEE Transactions on 26(1), 43–49 (1978)CrossRefzbMATHGoogle Scholar
  26. 26.
    LaValle, S. M.: Rapidly-Exploring Random Trees: A New Tool for Path Planning. Technical report, Computer Science Department. Iowa State University (1998)Google Scholar
  27. 27.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press (2006)Google Scholar
  28. 28.
    Melchior, N., Simmons, R.: Particle RRT for Path Planning with Uncertainty. In: 2007 IEEE International Conference on Robotics and Automation, pp 1617–1624 (2007)Google Scholar
  29. 29.
    Berg, J.V.D., Abbeel, P., Goldberg, K.: LQG-MP: Optimized Path Planning for Robots with Motion Uncertainty and Imperfect State Information. In: Proceedings of Robotics: Science and Systems (RSS), Zaragoza, Spain (2010)Google Scholar
  30. 30.
    Pivtoraiko, M., Kelly, A.: Constrained Motion Planning in Discrete State Spaces. In: Field and Service Robotics, pp 269–280 (2005)Google Scholar
  31. 31.
    Pivtoraiko, M., Kelly, A.: Efficient Constrained Path Planning via Search in State Lattices. In: Proceedings of the 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space (2005)Google Scholar
  32. 32.
    Michel, O.: Webots: Professional Mobile Robot Simulation. Journal of Advanced Robotics Systems 1(1), 39–42 (2004)Google Scholar
  33. 33.
    Biswas, J., Coltin, B., Veloso, M.: Corrective Gradient Refinement for Mobile Robot Localization. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2011)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Tekin Meriçli
    • 1
  • Manuela Veloso
    • 2
  • H. Levent Akın
    • 1
  1. 1.Department of Computer EngineeringBoğaziçi UniversityBebekTurkey
  2. 2.Computer Science DepartmentCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations