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International Journal of Social Robotics

Erschienen in:

23.11.2017

Teaching Robot Navigation Behaviors to Optimal RRT Planners

verfasst von: Noé Pérez-Higueras, Fernando Caballero, Luis Merino

Erschienen in: International Journal of Social Robotics | Ausgabe 2/2018

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Abstract

This work presents an approach for learning navigation behaviors for robots using Optimal Rapidly-exploring Random Trees (RRT\(^{*}\)) as the main planner. A new learning algorithm combining both Inverse Reinforcement Learning and RRT\(^{*}\) is developed to learn the RRT\(^{*}\)’s cost function from demonstrations. A comparison with other state-of-the-art algorithms shows how the method can recover the behavior from the demonstrations. Finally, a learned cost function for social navigation is tested in real experiments with a robot in the laboratory.

Vorheriger Artikel Avoiding the Content Treadmill for Robot Personalities

Nächster Artikel Analyzing the Impact of Different Feature Queries in Active Learning for Social Robots

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http://wiki.ros.org/move_base.

https://github.com/robotics-upo/upo_robot_navigation.

Abbeel P, Ng AY (2004) Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the twenty-first international conference on Machine learning, ICML ’04. ACM, New York, NY, USA, p 1. https://doi.org/10.1145/1015330.1015430

Argali B, Chernova S, Veloso M, Browning B (2009) A survey of robot learning from demonstrations. Robot Auton Syst 57:469–483CrossRef

Borgefors G (1986) Distance transformations in digital images. Comput. Vision Graph. Image Process. 34(3):344–371. https://doi.org/10.1016/S0734-189X(86)80047-0

Cristani M, Bazzani L, Paggetti G, Fossati A, Tosato D, Bue AD, Menegaz G, Murino V (2011) Andrea Fossati, Del\(^{\sim }\)Bue, A.: social interaction discovery by statistical analysis of F-formations. In: British machine vision conference (BMVC), pp 23.1–23.12. https://doi.org/10.5244/C.25.23

Feil-Seifer D, 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)

Finn C, Levine S, Abbeel P (2016) Guided cost learning: Deep inverse optimal control via policy optimization. In: Proceedings of the 33rd international conference on machine learning, vol 48

Fox D, Burgard W, Thrun S (1997) The dynamic window approach to collision avoidance. IEEE Robot Autom 4(1):23CrossRef

Hall ET (1990) The hidden dimension. Anchor

Henry P, Vollmer C, Ferris B, Fox D (2010) Learning to navigate through crowded environments. In: Proceedings of the IEEE international conference on robotics and automation (ICRA), pp 981–986

10.

Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30(7):846–894CrossRefMATH

11.

Kirby R, Forlizzi J, Simmons R (2010) Affective social robots. Robot Auton Syst 58:322–332CrossRef

12.

Kirby R, Simmons RG, Forlizzi J (2009) Companion: a constraint-optimizing method for person-acceptable navigation. In: RO-MAN, pp 607–612. IEEE

13.

Kretzschmar H, Kuderer M, Burgard W (2014) Learning to predict trajectories of cooperatively navigating agents. In: 2014 IEEE international conference on robotics and automation (ICRA) , pp 4015–4020. IEEE

14.

Kretzschmar H, Spies M, Sprunk C, Burgard W (2016) Socially compliant mobile robot navigation via inverse reinforcement learning. Int J Robot Res. https://doi.org/10.1177/0278364915619772

15.

Kruse T, Pandey AK, Alami R, Kirsch A (2013) Human-aware robot navigation: a survey. Robot Auton Syst 61(12):1726–1743. https://doi.org/10.1016/j.robot.2013.05.007

16.

Kuderer M, Gulati S, Burgard W (2015) Learning driving styles for autonomous vehicles from demonstration. In: Proceedings of the IEEE international conference on robotics & automation (ICRA), Seattle, USA, vol 134

17.

Kuderer M, Kretzschmar H, Sprunk C, Burgard W (2012) Feature-based prediction of trajectories for socially compliant navigation. In: Proceedings of robotics: science and systems (RSS). Sydney, Australia. http://www.informatik.uni-freiburg.de/~kudererm/publications/kuderer12rss.pdf

18.

Levine S, Koltun V (2012) Continuous inverse optimal control with locally optimal examples. In: ICML ’12: Proceedings of the 29th international conference on machine learning

19.

Luber M, Spinello L, Silva J, Arras KO (2012) Socially-aware robot navigation: A learning approach. In: 2012 IEEE/RSJ international conference on intelligent robots and systems, pp 902–907. https://doi.org/10.1109/IROS.2012.6385716

20.

Michini B, Cutler M, How JP (2013) Scalable reward learning from demonstration. In: IEEE international conference on robotics and automation (ICRA). IEEE. http://acl.mit.edu/papers/michini-icra-2013.pdf

21.

Ng AY, Russell SJ (2000) Algorithms for inverse reinforcement learning. In: Proceedings of the seventeenth international conference on machine learning, ICML ’00. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 663–670. http://dl.acm.org/citation.cfm?id=645529.657801

22.

Okal B, Arras KO (2016)Formalizing normative robot behavior. In: Social robotics: 8th international conference, ICSR 2016, Kansas City, MO, USA, November 1-3, 2016 Proceedings. Springer International Publishing, pp 62–71. https://doi.org/10.1007/978-3-319-47437-3_7

23.

Okal B, Arras KO (2016) Learning socially normative robot navigation behaviors with bayesian inverse reinforcement learning. In: Proceedings of the IEEE international conference on robotics and automation, ICRA , Stockholm, Sweden, pp 2889–2895. https://doi.org/10.1109/ICRA.2016.7487452

24.

Pacchierotti E, Christensen H, Jensfelt P (2006) Evaluation of passing distance for social robots. In: IEEE workshop on robot and human interactive communication (ROMAN). Hartfordshire, UK

25.

Pérez-Higueras N, Caballero F, Merino L (2016) Learning robot navigation behaviors by demonstration using a RRT* planner. In: International conference on social robotics. Springer International Publishing, pp 1–10

26.

Quigley M, Conley K, Gerkey BP, Faust J, Foote T, Leibs J, Wheeler R, Ng AY (2009) Ros: an open-source robot operating system. In: ICRA workshop on open source software

27.

Ramachandran D, Amir E (2007) Bayesian inverse reinforcement learning. In: Proceedings of the 20th international joint conference on artical intelligence vol 51, pp 2586–2591. http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-416.pdf

28.

Ramón-Vigo R, Pérez-Higueras N, Caballero F, Merino L (2014) Transferring human navigation behaviors into a robot local planner. In: Proceedings of the IEEE international symposium on robot and human interactive communication, RO-MAN. https://doi.org/10.1109/ROMAN.2014.6926347

29.

Ramon-Vigo R, Perez-Higueras N, Caballero F, Merino L (2015) Analyzing the relevance of features for a social navigation task. In: L.P. Reis, A.P. Moreira, P.U. Lima, L. Montano, V. Munoz-Martinez (eds.) Robot 2015: Second Iberian Robotics Conference, Advances in Intelligent Systems and Computing, vol. 418, pp. 235–246. Springer International Publishing. https://doi.org/10.1007/978-3-319-27149-1_19

30.

Ratliff ND, Bagnell JA, Zinkevich Ma (2006) Maximum margin planning. In: International conference on machine learning—ICML ’06(23), pp 729–736. https://doi.org/10.1145/1143844.1143936

31.

Ratliff ND, Silver D, Bagnell JA (2009) Learning to search: functional gradient techniques for imitation learning. Auton Robots 27(1):25–53. https://doi.org/10.1007/s10514-009-9121-3

32.

Setti F, Russell C, Bassetti C, Cristani M (2015) F-formation detection: individuating free-standing conversational groups in images. PLoS ONE 10(5):1–32. https://doi.org/10.1371/journal.pone.0123783

33.

Shiarlis K, Messias J, van Someren M, Whiteson S, Kim J, Vroon J, Englebienne G, Truong K, Evers V, Perez-Higueras N, Perez-Hurtado I, Ramon-Vigo R, Caballero F, Merino L, Shen J, Petridis S, Pantic M, Hedman L, Scherlund M, Koster R, Michel H (2015) Teresa: a socially intelligent semi-autonomous telepresence system. In: Workshop on machine learning for social robotics at ICRA-2015 in Seattle

34.

Shiarlis K, Messias J, Whiteson S (2017) Rapidly exploring learning trees. In: Proceedings of the IEEE international conference on robotics and automation (ICRA). IEEE, Singapore, Singapore

35.

Shiarlis K, Messias J, Whiteson S (2017) Acquiring social interaction behaviours for telepresence robots via deep learning from demonstration. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS). Vancouver, Canada

36.

Sisbot EA, Marin-Urias LF, Alami R, Siméon T (2007) A human aware mobile robot motion planner. IEEE Trans Robot 23(5):874–883CrossRef

37.

Vasquez D, Okal B, Arras KO (2014) Inverse reinforcement learning algorithms and features for robot navigation in crowds: an experimental comparison. In: Proceedings IEEE/RSJ international conference on Intelligent Robots and Systems (IROS), pp 1341–1346. https://doi.org/10.1109/IROS.2014.6942731

38.

Xia C, Kamel AE (2016) Neural inverse reinforcement learning in autonomous navigation. Robot Auton Syst 84:1–14. https://doi.org/10.1016/j.robot.2016.06.003

39.

Ziebart B, Maas A, Bagnell J, Dey A (2008) Maximum entropy inverse reinforcement learning. In: Proceedings of the national conference on artificial intelligence (AAAI)

Titel: Teaching Robot Navigation Behaviors to Optimal RRT Planners
verfasst von: Noé Pérez-Higueras
Fernando Caballero
Luis Merino
Publikationsdatum: 23.11.2017
Verlag: Springer Netherlands
Erschienen in: International Journal of Social Robotics / Ausgabe 2/2018
Print ISSN: 1875-4791
Elektronische ISSN: 1875-4805
DOI: https://doi.org/10.1007/s12369-017-0448-1

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