Robot Local Navigation with Learned Social Cost Functions

Noé Pérez-Higueras, Rafael Ramón-Vigo, Fernando Caballero, Luis Merino

Abstract

Robot navigation in human environments is an active research area that poses serious challenges. Among them, human-awareness has gain lot of attention in the last years due to its important role in human safety and robot acceptance. The proposed robot navigation system extends state of the navigation schemes with some social skills in order to naturally integrate the robot motion in crowded areas. Learning has been proposed as a more principled way of estimating the insights of human social interactions. To do this, inverse reinforcement learning is used to derive social cost functions by observing persons walking through the streets. Our objective is to incorporate such costs into the robot navigation stack in order to “emulate” these human interactions. In order to alleviate the complexity, the system is focused on learning an adequate cost function to be applied at the local navigation level, thus providing direct low-level controls to the robot. The paper presents an analysis of the results in a robot navigating in challenging real scenarios, analyzing and comparing this approach with other algorithms.

References

  1. Abbeel, P. and Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning, ICML 7804, pages 1-, New York, NY, USA. ACM.
  2. Alili, S., Warnier, M., Ali, M., and Alami, R. (2009). Planning and plan-execution for human-robot cooperative task achievement. In 19th International Conference on Automated Planning and Scheduling.
  3. Argali, B., Chernova, S., Veloso, M., and Browning, B. (2009). A survey of robot learning from demonstrations. Robotics and Autonomous Systems, 57:469- 483.
  4. Arras, K. O., Mozos, O. M., and Burgard, W. (2008). Using boosted features for the detection of people in 2d range data. In Proc. International Conference on Robotics and Automation, ICRA.
  5. Carballo, A., Ohya, A., and Yuta, S. (2010). People detection using range and intensity data from multi-layered laser range finders. In Proc. International Conference on Intelligent Robots and Systems, IROS, pages 5849- 5854.
  6. Clodic, A., Cao, H., Alili, S., Montreuil, V., Alami, R., and Chatila, R. (2008). SHARY: A Supervision System Adapted to Human-Robot Interaction. In Khatib, O., Kumar, V., and Pappas, G. J., editors, Experimental Robotics, The Eleventh International Symposium, ISER 2008, July 13-16, 2008, Athens, Greece, volume 54 of Springer Tracts in Advanced Robotics, pages 229-238. Springer.
  7. Enzweiler, M. and Gavrila, D. (2008). Integrated pedestrian classification and orientation estimation. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition.
  8. Feil-Seifer, D. and Mataric, M. (2011). People-aware navigation for goal-oriented behavior involving a human partner. In Proceedings of the IEEE International Conference on Development and Learning (ICDL).
  9. Gerkey, B. and Konolige, K. (2008). Planning and control in unstructured terrain. In Workshop on Path Planning on Costmaps, Proceedings of the IEEE International Conference on Robotics and Automation.
  10. Hall, E. T. (1990). The Hidden Dimension. Anchor.
  11. Henry, P., Vollmer, C., Ferris, B., and Fox, D. (2010). Learning to navigate through crowded environments. In ICRA'10, pages 981-986.
  12. Keller, C., Enzweiler, M., Rohrbach, M., Llorca, D.-F., Schnörr, C., and Gavrila, D. (2011). The benefits of dense stereo for pedestrian detection. IEEE Trans. on Intelligent Transportation Systems, 12(4):1096-1106.
  13. Kirby, R., J. Forlizzi, J., and Simmons, R. (2010). Affective social robots. Robotics and Autonomous Systems, 58:322-332.
  14. Kirby, R., Simmons, R. G., and Forlizzi, J. (2009). Companion: A constraint-optimizing method for personacceptable navigation. In RO-MAN, pages 607-612. IEEE.
  15. Kruse, T., Pandey, A. K., Alami, R., and Kirsch, A. (2013). Human-aware robot navigation: A survey. Robot. Auton. Syst., 61(12):1726-1743.
  16. Levine, S., Popovic, Z., and Koltun, V. (2011). Nonlinear inverse reinforcement learning with gaussian processes. In Neural Information Processing Systems Conference.
  17. Luber, M., Spinello, L., Silva, J., and Arras, K. (2012). Socially-aware robot navigation: A learning approach. In IROS, pages 797-803. IEEE.
  18. Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B. P., and Konolige, K. (2010). The Office Marathon: Robust Navigation in an Indoor Office Environment. In International Conference on Robotics and Automation.
  19. Pellegrini, S., Ess, A., Schindler, K., and van Gool, L. (2009). You'll never walk alone: Modeling social behavior for multi-target tracking. In International Conference on Computer Vision.
  20. Ramon-Vigo, R., Perez-Higueras, N., Caballero, F., and Merino, L. (2014). Transferring human navigation behaviors into a robot local planner. In RO-MAN. In Press.
  21. Rios-Martinez, J., Spalanzani, A., and Laugier, C. (2011). Understanding human interaction for probabilistic autonomous navigation using risk-rrt approach. In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages 2014-2019.
  22. Siegwart, R., Arras, K. O., Bouabdallah, S., Burnier, D., Froidevaux, G., Greppin, X., Jensen, B., Lorotte, A., Mayor, L., Meisser, M., Philippsen, R., Piguet, R., Ramel, G., Terrien, G., and Tomatis, N. (2003). Robox at Expo.02: A large-scale installation of personal robots. Robotics and Autonomous Systems, 42(3-4):203-222.
  23. Sisbot, E. A., Marin-Urias, L. F., Alami, R., and Siméon, T. (2007). A Human Aware Mobile Robot Motion Planner. IEEE Transactions on Robotics, 23(5):874-883.
  24. Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A. B., Dellaert, F., Fox, D., and Hahnel, C. (2000). Probabilistic algorithms and the interactive museum tour-guide robot minerva. The International Journal of Robotics Research, 19:972-999.
  25. Tipaldi, G. D. and Arras, K. O. (2011). Planning Problems for Social Robots. In Proceedings fo the Twenty-First Internacional Conference on Automated Planning and Scheduling, pages 339-342.
  26. Trautman, P. and Krause, A. (2010). Unfreezing the robot: Navigation in dense, interacting crowds. In IROS, pages 797-803. IEEE.
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Paper Citation


in Harvard Style

Pérez-Higueras N., Ramón-Vigo R., Caballero F. and Merino L. (2014). Robot Local Navigation with Learned Social Cost Functions . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 618-625. DOI: 10.5220/0005120806180625


in Bibtex Style

@conference{icinco14,
author={Noé Pérez-Higueras and Rafael Ramón-Vigo and Fernando Caballero and Luis Merino},
title={Robot Local Navigation with Learned Social Cost Functions},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={618-625},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005120806180625},
isbn={978-989-758-040-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Robot Local Navigation with Learned Social Cost Functions
SN - 978-989-758-040-6
AU - Pérez-Higueras N.
AU - Ramón-Vigo R.
AU - Caballero F.
AU - Merino L.
PY - 2014
SP - 618
EP - 625
DO - 10.5220/0005120806180625