DOI QR코드

DOI QR Code

운동계획을 위한 입자 군집 최적화를 이용한 시범에 의한 학습의 적응성 향상

Adaptability Improvement of Learning from Demonstration with Particle Swarm Optimization for Motion Planning

  • 투고 : 2016.09.12
  • 심사 : 2016.11.01
  • 발행 : 2016.12.31

초록

We present a method for improving adaptability of Learning from Demonstration (LfD) strategy by combining the LfD and Particle Swarm Optimization (PSO). A trajectory generated from an LfD is modified with PSO by minimizing a fitness function that considers constraints. Finally, the final trajectory is suitable for a task and adapted for constraints. The effectiveness of the method is shown with a target reaching task with a manipulator in three-dimensional space.

키워드

참고문헌

  1. S. M. LaValle, "Rapidly-exploring random trees: A new tool for path planning," TR 98-11, Computer Science Dept., Iowa State University, 1998.
  2. S. M. LaValle, "Planning algorithms," Cambridge University Press, 2006.
  3. A. Billard, S. Calinon, R. Dillmann, S.Schaal, "Robot programming by Demonstration," Handbook of Robotics, Springer, pp. 1371-1394, 2008.
  4. B. D. Argall, S. Chernova, M. Veloso, and B. Browning, "A survey of robot learning from demonstration," Robotics and Autonomous Systems, vol. 57, no. 5, pp. 469-483, 2009. https://doi.org/10.1016/j.robot.2008.10.024
  5. A. Ude, A. Gams, T. Asfour, J. Morimoto, "Task-Specific generalization of discrete and periodic dynamic movement primitives," IEEE Tran. on Robotics, vol. 26, no. 5, 2010.
  6. A. J. Ijspeert, J. Nakanishi, and S. Schaal, "Movement imitation with nonlinear dynamical systems in humanoid robots," in Proc. IEEE Int. Conf. Robot. Autom., pp. 1398-1403, 2002.
  7. A. J. Ijspeert, J. Nakanishi, and S. Schaal, "Learning rhythmic movements by demonstration using nonlinear oscillators," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 958-963, 2002.
  8. D. Forte, A. Gams, J. Morimoto, A. Ude, "On-line motion synthesis and adaptation using a trajectory database," Robotics and Autonomous Systems, vol. 60, pp. 1327-1339, 2012. https://doi.org/10.1016/j.robot.2012.05.004
  9. S. Calinon, F. Guenter, and A. Billard, "On learning, representing, and generalizing a task in a humanoid robot," IEEE Trans. on Systems, Man, and Cyber., Part B: Cyber., vol. 37 pp. 286-298, 2007. https://doi.org/10.1109/TSMCB.2006.886952
  10. S. Calinon, and A. Billard, "A probabilistic programming by demonstration framework handling constraints in joint space and task space," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 367-372, 2008.
  11. S. Calinon, F. D'halluin, D. G. Caldwell, and A. Billard, 2009, "Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework," in Proc. IEEE/RAS Int. Conf. Humanoids, pp. 582-588, 2009
  12. J. Kennedy, R. Eberhart, "Particle swarm optimization," Proc. of the IEEE Int. Conf. on Neural Networks, vol. 4, pp. 1942-1948, 1995.
  13. A. Abraham, H. Guo, and H. Liu, "Swarm intelligence: foundations, perspectives and applications," Stu. in Compu. Inte., Springer, vol 26, pp. 3-25, 2006.
  14. R. Krohling, "Gaussian swarm: a novel particle swarm optimization algorithm," IEEE Conf. on Cyber. and Int. Sys., vol. 1, pp. 372-376, Dec. 2004.
  15. J.-J Kim, S.-Y. Park, and J.-J. Lee, "Adaptability improvement of learning from demonstration with sequential quadratic programming for motion planning," IEEE Conf. on Adv. Int. Mech., pp. 1032-1037, 2015.
  16. C. E. Rasmussen and C. K. I. Williams, "Gaussian processes for machine learning," MIT Press, 2006.
  17. C. G. Atkeson, A.W. Moore, and S. Schaal, "Locally weighted learning," Artif. Intell. Rev., vol. 11, pp. 75-113, 1997. https://doi.org/10.1023/A:1006511328852
  18. T. Flash, N. Hogan, "The coordination of arm movements: an experimentally confirmed mathematical model," The Journal of Neuroscience, vol. 5, no. 7, pp. 1688-1703, 1985. https://doi.org/10.1523/JNEUROSCI.05-07-01688.1985