A System for Multi-step Mobile Manipulation: Architecture, Algorithms, and Experiments

  • Siddhartha S. Srinivasa
  • Aaron M. Johnson
  • Gilwoo Lee
  • Michael C. Koval
  • Shushman Choudhury
  • Jennifer E. King
  • Christopher M. Dellin
  • Matthew Harding
  • David T. Butterworth
  • Prasanna Velagapudi
  • Allison Thackston
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 1)

Abstract

Household manipulation presents a challenge to robots because it requires perceiving a variety of objects, planning multi-step motions, and recovering from failure. This paper presents practical techniques that improve performance in these areas by considering the complete system in the context of this specific domain. We validate these techniques on a table-clearing task that involves loading objects into a tray and transporting it. The results show that these techniques improve success rate and task completion time by incorporating expected real-world performance into the system design.

Keywords

Task and motion planning Mobile manipulation 

References

  1. 1.
    Srinivasa, S., Ferguson, D., et al.: The robotic busboy: steps towards developing a mobile robotic home assistant. In: International Conference on Intelligent Autonomous Systems (2008)Google Scholar
  2. 2.
    Cakmak, M., Srinivasa, S., et al.: Human preferences for robot-human hand-over configurations. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2011)Google Scholar
  3. 3.
    King, J., Klingensmith, M., et al.: Pregrasp manipulation as trajectory optimization. In: Robotics: Science and Systems (2013)Google Scholar
  4. 4.
    Moll, M., Şucan, I.A., Kavraki, L.E.: An extensible benchmarking infrastructure for motion planning algorithms, CoRR abs/1412.6673 (2014)Google Scholar
  5. 5.
    Kaelbling, L.P., Lozano-Pérez, T.: Integrated task and motion planning in belief space. Int. J. Robotics Res. 32(9–10), 1194–1227 (2013)CrossRefGoogle Scholar
  6. 6.
    Hebert, P., Bajracharya, M., et al.: Mobile manipulation and mobility as manipulation–design and algorithms of RoboSimian. J. Field Robotics 32(2), 255–274 (2015)CrossRefGoogle Scholar
  7. 7.
    Bagnell, J.A., Cavalcanti, F., et al.: An integrated system for autonomous robotics manipulation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2012)Google Scholar
  8. 8.
    Bobrow, J.E., Dubowsky, S., Gibson, J.: Time-optimal control of robotic manipulators along specified paths. Int. J. Robotics Res. 4(3), 3–17 (1985)CrossRefGoogle Scholar
  9. 9.
    Geraerts, R., Overmars, M.H.: Creating high-quality paths for motion planning. Int. J. Robotics Res. 26(8), 845–863 (2007)CrossRefGoogle Scholar
  10. 10.
    Olson, E.: Apriltag: a robust and flexible visual fiducial system. In: IEEE International Conference on Robotics and Automation, pp. 3400–3407 (2011)Google Scholar
  11. 11.
    Pauwels, K., Kragic, D.: Simtrack: A simulation-based framework for scalable real-time object pose detection and tracking. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1300–1307 (2015)Google Scholar
  12. 12.
    Cao, Z., Sheikh, Y., Banerjee, N.K.: Real-time scalable 6-DOF pose estimation for textureless objects. In: IEEE International Conference on Robotics and Automation, pp. 2441–2448 (2016)Google Scholar
  13. 13.
    Klingensmith, M., Dryanovski, I., et al.: Chisel: real time large scale 3D reconstruction onboard a mobile device. In: Robotics: Science and Systems (2015)Google Scholar
  14. 14.
    Chitta, S., Jones, E.G., et al.: Mobile manipulation in unstructured environments: perception, planning, and execution. IEEE Robot. Autom. Mag. 19(2), 58–71 (2012)CrossRefGoogle Scholar
  15. 15.
    Correll, N., Bekris, K.E., et al.: Lessons from the Amazon picking challenge. CoRR abs/1601.05484 (2016)Google Scholar
  16. 16.
    Gini, M., Smith, R.: Monitoring robot actions for error detection and recovery. In: Workshop on Space Telerobotics (1987)Google Scholar
  17. 17.
    Edsinger, A.L.: Robot manipulation in human environments. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (2007)Google Scholar
  18. 18.
    Shapiro, E.: A hierarchical framework for configuration space task planning. Masters Thesis, Carnegie Mellon University, Computer Science Department (2015)Google Scholar
  19. 19.
    Smith, R.: Open Dynamics Engine (ODE). http://www.ode.org/
  20. 20.
    Larsen, E., Gottschalk, S.: Proximity Query Package (PQP), University of North Carolina at Chapel Hill (1999). http://gamma.cs.unc.edu/SSV/
  21. 21.
    Pan, J., Chitta, S., Manocha, D.: FCL: a general purpose library for collision and proximity queries. In: IEEE International Conference on Robotics and Automation, pp. 3859–3866, May 2012Google Scholar
  22. 22.
    Zucker, M., Ratliff, R., et al.: CHOMP: covariant Hamiltonian optimization for motion planning. Int. J. Robotics Res. 32(9–10), 1164–1193 (2013)CrossRefGoogle Scholar
  23. 23.
    Schulman, J., Ho, J., et al.: Finding locally optimal, collision-free trajectories with sequential convex optimization. In: Robotics: Science and Systems, pp. 1–10 (2013)Google Scholar
  24. 24.
    Kuffner, J.J., LaValle, S.M.: RRT-Connect: an efficient approach to single-query path planning. In: IEEE International Conference on Robotics and Automation (2000)Google Scholar
  25. 25.
    Berenson, D., Srinivasa, S.S., et al.: Manipulation planning on constraint manifolds. In: IEEE International Conference on Robotics and Automation (2009)Google Scholar
  26. 26.
    Berenson, D., Srinivasa, S., Kuffner, J.: Task space regions: a framework for pose-constrained manipulation planning. Int. J. Robotics Res. 30(12), 1435–1460 (2011)CrossRefGoogle Scholar
  27. 27.
    Dragan, A., Ratliff, N., Srinivasa, S.: Manipulation planning with goal sets using constrained trajectory optimization. In: IEEE International Conference on Robotics and Automation (2011)Google Scholar
  28. 28.
    Hauser, K., Ng-Thow-Hing, V.: Fast smoothing of manipulator trajectories using optimal bounded-acceleration shortcuts. In: IEEE International Conference on Robotics and Automation, pp. 2493–2498 (2010)Google Scholar
  29. 29.
    Berenson, D.: Constrained manipulation planning. Ph.D. thesis, Carnegie Mellon University, Robotics Institute, May 2011Google Scholar
  30. 30.
    Kuffner, J.J., LaValle, S.M.: RRT-Connect: an efficient approach to single-query path planning. In: IEEE International Conference on Robotics and Automation, vol. 2, pp. 995–1001 (2000)Google Scholar
  31. 31.
    Şucan, I.A., Moll, M., Kavraki, L.E.: The open motion planning library. IEEE Robot. Autom. Mag. 19(4), 72–82 (2012). http://ompl.kavrakilab.org

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Siddhartha S. Srinivasa
    • 1
  • Aaron M. Johnson
    • 1
  • Gilwoo Lee
    • 1
  • Michael C. Koval
    • 1
  • Shushman Choudhury
    • 1
  • Jennifer E. King
    • 1
  • Christopher M. Dellin
    • 1
  • Matthew Harding
    • 1
  • David T. Butterworth
    • 1
  • Prasanna Velagapudi
    • 1
  • Allison Thackston
    • 2
  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Toyota Motor Engineering & Manufacturing North AmericaSan JoseUSA

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