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Autonomous Robots

, Volume 39, Issue 2, pp 199–217 | Cite as

Adaptation of manipulation skills in physical contact with the environment to reference force profiles

  • Fares J. Abu-Dakka
  • Bojan Nemec
  • Jimmy A. Jørgensen
  • Thiusius R. Savarimuthu
  • Norbert Krüger
  • Aleš Ude
Article

Abstract

We propose a new methodology for learning and adaption of manipulation skills that involve physical contact with the environment. Pure position control is unsuitable for such tasks because even small errors in the desired trajectory can cause significant deviations from the desired forces and torques. The proposed algorithm takes a reference Cartesian trajectory and force/torque profile as input and adapts the movement so that the resulting forces and torques match the reference profiles. The learning algorithm is based on dynamic movement primitives and quaternion representation of orientation, which provide a mathematical machinery for efficient and stable adaptation. Experimentally we show that the robot’s performance can be significantly improved within a few iteration steps, compensating for vision and other errors that might arise during the execution of the task. We also show that our methodology is suitable both for robots with admittance and for robots with impedance control.

Keywords

Skill learning and adaptation Manipulation and compliant assembly Programming by demonstration Physical human-robot interaction 

Notes

Acknowledgments

The research leading to these results has received funding from the European Community Seventh Framework Programme FP7/2007-2013 (Specific Programme Cooperation, Theme 3, Information and Communication Technologies) under Grant Agreement No. 269959, IntellAct and No. 600578, ACAT.

Supplementary material

Supplementary material 1 (mp4 2450 KB)

Supplementary material 2 (mp4 6842 KB)

References

  1. Bristow, D., Tharayil, M., & Alleyne, A. (2006). A survey of iterative learning control. IEEE Control Systems Magazine, 26(3), 96–114.CrossRefGoogle Scholar
  2. Broenink, J.F., & Tiernego, M.L.J. (1996). Peg-in-hole assembly using impedance control with a 6 DOF robot. Proceedings of the 8th European Simulation Symposium (pp. 504–508).Google Scholar
  3. Bruyninckx, H., Dutre, S., & De Schutter, J. (1995). Peg-on-hole: a model based solution to peg and hole alignment. IEEE International Conference on Robotics and Automation (ICRA), (Vol. 2, pp. 1919–1924). Nagoya, Japan.Google Scholar
  4. Buchli, J., Stulp, F., Theodorou, E., & Schaal, S. (2011). Learning variable impedance control. International Journal of Robotics Research, 30(7), 820–833.CrossRefGoogle Scholar
  5. Calinon, S., Evrard, P., Gribovskaya, E., Billard, A., & Kheddar, A. (2009). Learning collaborative manipulation tasks by demonstration using a haptic interface. IEEE International Conference on Advanced Robotics (ICAR), Munich, Germany.Google Scholar
  6. Collins, K., Palmer, A. J., & Rathmill, K. (1985). The development of a European benchmark for the comparison of assembly robot programming systems. In K. Rathmill, P. MacConail, S. O’leary, & J. Browne (Eds.), Robot technology and applications (pp. 187–199). New York: Springer.CrossRefGoogle Scholar
  7. Dillmann, R. (2004). Teaching and learning of robot tasks via observation of human performance. Robotics and Autonomous Systems, 47(2–3), 109–116.CrossRefGoogle Scholar
  8. Giordano, P. R., Stemmer, A., Arbter, K., & Albu-Schäffer, A. (2008). Robotic assembly of complex planar parts: An experimental evaluation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3775–3782). Nice, France.Google Scholar
  9. Gullapalli, V., Grupen, R. A., & Barto, A. G. (1992). Learning reactive admittance control. IEEE International Conference on Robotics and Automation (ICRA) (pp. 1475–1480). Nice, France.Google Scholar
  10. Hamner, B., Koterba, S., Shi, J., Simmons, R., & Singh, S. (2010). An autonomous mobile manipulator for assembly tasks. Autonomous Robots, 28, 131–149.CrossRefGoogle Scholar
  11. Hersch, M., Guenter, F., Calinon, S., & Billard, A. (2008). Dynamical system modulation for robot learning via kinesthetic demonstrations. IEEE Transactions on Robotics, 24(6), 1463–1467.CrossRefGoogle Scholar
  12. Hirana, K., Suzuki, T., & Okuma, S. (2002). Optimal motion planning for assembly skill based on mixed logical dynamical system. textit7th International Workshop on Advanced Motion Control (pp. 359–364). Maribor, Slovenia.Google Scholar
  13. Hogan, N. (1985). Impedance control: An approach to manipulation: Part I—theory. Journal of Dynamic Systems, Measurement, and Control, 107(1), 1–7.CrossRefzbMATHGoogle Scholar
  14. Hsu, P., Hauser, J., & Sastry, S. (1989). Dynamic control of redundant manipulators. Journal of Robotic Systems, 6(2), 133–148.CrossRefGoogle Scholar
  15. Hutter, M., Hoepflinger, M.A., Gehring, C., Bloesch, M., Remy, C.D., & Siegwart, R. (2012). Hybrid operational space control for compliant legged systems. Robotics: Science and Systems (RSS). Sydney, Australia.Google Scholar
  16. Hyon, S. H., Hale, J. G., & Cheng, G. (2007). Full-body compliant human-humanoid interaction: Balancing in the presence of unknown external forces. IEEE Transactions on Robotics, 23(5), 884–898.CrossRefGoogle Scholar
  17. Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2001). Nonlinear dynamical systems for imitation with humanoid robots. IEEE-RAS International Conference on Humanoid Robots (Humanoids) (pp. 219–226). Tokyo, Japan.Google Scholar
  18. Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., & Schaal, S. (2013). Dynamical movement primitives: Learning attractor models for motor behaviors. Neural Computations, 25(2), 328–373.MathSciNetCrossRefzbMATHGoogle Scholar
  19. Kaiser, M., & Dillmann, R. (1996). Building elementary robot skills from human demonstration. IEEE International Conference on Robotics and Automation (ICRA) (pp. 2700–2705). Minneapolis, MN.Google Scholar
  20. Kalakrishnan, M., Righetti, L., Pastor, P., & Schaal, S. (2011). Learning force control policies for compliant manipulation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4639–4644). San Francisco, CA.Google Scholar
  21. Kawato, M. (1990). Feedback-error-learning neural network for supervised motor learning. In E. Eckmiller (Ed.), Advanced neural computers (pp. 365–372). Amsterdam: Elsevier.Google Scholar
  22. Kazemi, M., Valois, J. S., Bagnell, J. A., & Pollard, N. (2014). Human-inspired force compliant grasping primitives. Autonomous Robots, 37, 209–225.CrossRefGoogle Scholar
  23. Khatib, O. (1987). A unified approach for motion and force control of robot manipulators: The operational space formulation. IEEE Journal of Robotics and Automation, RA–3(1), 43–53.CrossRefGoogle Scholar
  24. Kormushev, P., Calinon, S., & Caldwell, D. G. (2011). Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input. Advanced Robotics, 25(5), 581–603.CrossRefGoogle Scholar
  25. Laurin-Kovitz, K. F., Colgate, J. E., & Carnes, S. D. R. (1991). Design of components for programmable passive impedance. IEEE International Conference on Robotics and Automation (ICRA) (pp. 1476–1481). Sacramento, CA.Google Scholar
  26. Lee, D., & Ott, C. (2011). Incremental kinesthetic teaching of motion primitives using the motion refinement tube. Autonomous Robots, 31(2), 115–131.MathSciNetCrossRefGoogle Scholar
  27. Li, Y. (1997). Hybrid control approach to the peg-in-hole problem. IEEE Robotics and Automation Magazine, 4(2), 52–60.CrossRefGoogle Scholar
  28. Lopes, A., & Almeida, F. (2008). A force-impedance controlled industrial robot using an active robotic auxiliary device. Robotics and Computer-Integrated Manufacturing, 24, 299–309.CrossRefGoogle Scholar
  29. Moore, K., Chen, Y., & Ahn, H. S. (2006). Iterative learning control: A tutorial and big picture view. 45th IEEE Conference on Decision and Control (pp. 2352–2357). San Diego, CA.Google Scholar
  30. Nakamura, Y. (1991). Advanced robotics: Redundancy and optimization. Boston, MA: Addison-Wesley.Google Scholar
  31. Nakanishi, J., & Schaal, S. (2004). Feedback error learning and nonlinear adaptive control. Neural Networks, 17, 1453–1465.CrossRefzbMATHGoogle Scholar
  32. Nakanishi, J., Cory, R., Mistry, M., Peters, J., & Schaal, S. (2008). Operational space control: A theoretical and empirical comparison. The International Journal of Robotics Research, 27, 737– 757.CrossRefGoogle Scholar
  33. Nemec, B., & Ude, A. (2012). Action sequencing using dynamic movement primitives. Robotica, 30, 837–846.CrossRefGoogle Scholar
  34. Nemec, B., Žlajpah, L., & Omrčen, D. (2007). Comparison of null-space and minimal null-space control algorithms. Robotica, 25(5), 511–520.CrossRefGoogle Scholar
  35. Newman, W. S., Branicky, M. S., Podgurski, H. A., Chhatpar, S., Huang, L., Swaminathan, J., & Zhang, H. (1999). Force-responsive robotic assembly of transmission components. IEEE International Conference on Robotics and Automation (ICRA), (Vol. 3, pp. 2096–2102). Detroit, Michigan.Google Scholar
  36. Pastor, P., Righetti, L., Kalakrishnan, M., & Schaal, S. (2011). Online movement adaptation based on previous sensor experiences. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 365–371). San Francisco, CA.Google Scholar
  37. Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibsz, J., Bergery, E., Wheeler, R., & Ng, A. (2009). ROS: An open-source robot operating system. ICRA Workshop on Open Source Software. Kobe, Japan.Google Scholar
  38. Raibert, M. H., & Craig, J. J. (1981). Hybrid position/force control of manipulators. Journal of Dynamic Systems, Measurement, and Control, 103(2), 126–133.CrossRefGoogle Scholar
  39. Rozo, L., Jiménez, P., & Torras, C. (2013). A robot learning from demonstration framework to perform force-based manipulation tasks. Inteligent Service Robotics, 6, 33–51.CrossRefGoogle Scholar
  40. Rusu, R. B., & Cousins, S. (2011). 3D is here: Point Cloud Library (PCL). IEEE International Conference on Robotics and Automation (ICRA). Shanghai: ICRA Communications.Google Scholar
  41. Savarimuthu, T. R., Liljekrans, D., Ellekilde, L. P., Ude, A., Nemec, B., & Krüger, N. (2013). Analysis of human peg-in-hole executions in a robotic embodiment using uncertain grasps. 9th International Workshop on Robot Motion and Control (pp. 233–239). Poland: Wasowo.Google Scholar
  42. Schreiber, G., Stemmer, A., & Bischoff, R. (2010). The fast research interface for the KUKA lightweight robot. ICRA Workshop on Innovative Robot Control Architectures for Demanding (Research) Applications—How to Modify and Enhance Commercial Controllers. Anchorage, Alaska.Google Scholar
  43. Skubic, M., & Volz, R. A. (1998). Learning force-based assembly skills from human demonstration for execution in unstructured environments. IEEE International Conference on Robotics and Automation (ICRA) (pp. 1281–1288). Leuven, Belgium.Google Scholar
  44. Stemmer, A., Albu-Schäffer, A., & Hirzinger, G. (2007). An analytical method for the planning of robust assembly tasks of complex shaped planar parts. IEEE International Conference on Robotics and Automation (ICRA) (pp. 317–323). RomeGoogle Scholar
  45. Ude, A. (1999). Filtering in a unit quaternion space for model-based object tracking. Robotics and Autonomous Systems, 28(2–3), 163–172.CrossRefGoogle Scholar
  46. Ude, A., Gams, A., Asfour, T., & Morimoto, J. (2010). Task-specific generalization of discrete and periodic dynamic movement primitives. IEEE Transactions on Robotics, 26(5), 800–815.CrossRefGoogle Scholar
  47. Villani, L., & De Schutter, J. (2008). Force control. In B. Siciliano & O. Khatib (Eds.), Springer handbook of robotics (pp. 161–185). Berlin: Springer.CrossRefGoogle Scholar
  48. Whitney, D. E. (1969). Resolved motion rate control of manipulators and human prostheses. IEEE Transactions on Systems, Man, and Cybernetics, MMS–10(2), 47–53.MathSciNetGoogle Scholar
  49. Whitney, D. E., & Nevins, J. L. (1979). What is the Remote Center Compliance (RCC) and what can it do? International Symposium on Industrial Robots (ISIR). Washington, DC.Google Scholar
  50. Xiao, J. (1997). Goal-contact relaxation graphs for contact-based fine motion planning. IEEE International Symposium on Assembly and Task Planning (ISATP) (pp. 25–30). California: Marina del Rey.Google Scholar
  51. Yamashita, T., Godler, I., Takahashi, Y., Wada, K., & Katoh, R. (1991). Peg-and-hole task by robot with force sensor: Simulation and experiment. International Conference on Industrial Electronics, Control and Instrumentation (IECON) (pp. 980–985). Kobe.Google Scholar
  52. Yoshikawa, T. (2000). Force control of robot manipulators. IEEE International Conference on Robotics and Automation (ICRA), (pp. 220–226). San Francisco, CA.Google Scholar
  53. Yun, S. K. (2008). Compliant manipulation for peg-in-hole: Is passive compliance a key to learn contact motion? In IEEE International Conference on Robotics and Automation (ICRA) (pp. 1647–1652). Pasadena, CA.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Fares J. Abu-Dakka
    • 1
  • Bojan Nemec
    • 1
  • Jimmy A. Jørgensen
    • 2
  • Thiusius R. Savarimuthu
    • 2
  • Norbert Krüger
    • 2
  • Aleš Ude
    • 1
  1. 1.Humanoid and Cognitive Robotics Lab, Department of Automatics, Biocybernetics and RoboticsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Cognitive Vision Lab, Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkOdenseDenmark

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