Socially Acceptable Robot Behavior
  • ISSN 1572-0373
  • E-ISSN: 1572-0381
Buy:$35.00 + Taxes



Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort.


Article metrics loading...

Loading full text...

Full text loading...


  1. Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S.
    (2016) Social lstm: Human trajectory prediction in crowded spaces. In2016 ieee conference on computer vision and pattern recognition (cvpr) (p.961–971). 10.1109/CVPR.2016.110
    https://doi.org/10.1109/CVPR.2016.110 [Google Scholar]
  2. Arjovsky, M., Chintala, S., & Bottou, L.
    (2017) Wasserstein gan.
    [Google Scholar]
  3. Asghari Oskoei, M., Walters, M., & Dautenhahn, K.
    (2010) An autonomous proxemic system for a mobile companion robot. InProceedings of the aisb 2010 symposium on new frontiers for human robot interaction. Leicester, UK.
    [Google Scholar]
  4. Biswas, A., Wang, A., Silvera, G., Steinfeld, A., & Admoni, H.
    (2022) Socnavbench: A grounded simulation testing framework for evaluating social navigation. ACM Transactions on Human-Robot Interaction (THRI), 11(3), 1–24. 10.1145/3476413
    https://doi.org/10.1145/3476413 [Google Scholar]
  5. Borenstein, J., Koren, Y.,
    (1991) The vector field histogram-fast obstacle avoidance for mobile robots. IEEE transactions on robotics and automation, 7(3), 278–288. 10.1109/70.88137
    https://doi.org/10.1109/70.88137 [Google Scholar]
  6. Brooks, R.
    (1986) A robust layered control system for a mobile robot. IEEE journal on robotics and automation, 2(1), 1423. 10.1109/JRA.1986.1087032
    https://doi.org/10.1109/JRA.1986.1087032 [Google Scholar]
  7. Burda, Y., Edwards, H., Storkey, A., & Klimov, O.
    (2018) Exploration by random network distillation. ar Xiv preprint arXiv:1810.12894.
    [Google Scholar]
  8. Burgard, W., Cremers, A., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., … Thrun, S.
    (1999) Experiences with an interactive museum tour-guide robot. Artif. Intell., 1141, 3–55. 10.1016/S0004‑3702(99)00070‑3
    https://doi.org/10.1016/S0004-3702(99)00070-3 [Google Scholar]
  9. Cai, K., Wang, C., Cheng, J., De Silva, C. W., & Meng, M. Q.-H.
    (2020) Mobile Robot Path Planning in Dynamic Environments: A Survey. arXiv preprint arXiv:2006.14195.
    [Google Scholar]
  10. Che, Y., Okamura, A. M., & Sadigh, D.
    (2020) Efficient and trustworthy social navigation via explicit and implicit robot-human communication. IEEE Transactions on Robotics, 36(3), 692–707. 10.1109/TRO.2020.2964824
    https://doi.org/10.1109/TRO.2020.2964824 [Google Scholar]
  11. Chen, Y. F., Everett, M., Liu, M., & How, J. P.
    (2017) Socially aware motion planning with deep reinforcement learning. CoRR, abs/1703.08862. Retrieved fromarxiv.org/abs/1783.88862. 10.1109/IROS.2017.8202312
    https://doi.org/10.1109/IROS.2017.8202312 [Google Scholar]
  12. Dudek, G., & Jenkin, M.
    (2010) Computational principles of mo-bile robotics. Cambridge university press. 10.1017/CBO9780511780929
    https://doi.org/10.1017/CBO9780511780929 [Google Scholar]
  13. Farina, F., Fontanelli, D., Garulli, A., Giannitrapani, A., & Prat-tichizzo, D.
    (2017) Walking ahead: The headed social force model. PloS one, 12(1), e0169734. 10.1371/journal.pone.0169734
    https://doi.org/10.1371/journal.pone.0169734 [Google Scholar]
  14. Ferrer, G., Garrell, A., & Sanfeliu, A.
    (2013) Robot companion: A social-force based approach with human awareness-navigation in crowded environments. In2013 ieee/rsj international conference on intelligent robots and systems (pp. 1688–1694). 10.1109/IROS.2013.6696576
    https://doi.org/10.1109/IROS.2013.6696576 [Google Scholar]
  15. Festo Robotics, R. [Google Scholar]
  16. Fong, T., Nourbakhsh, I., & Dautenhahn, K.
    (2003) A survey of socially interactive robots. Robotics and autonomous systems, 42(3–4), 143–166. 10.1016/S0921‑8890(02)00372‑X
    https://doi.org/10.1016/S0921-8890(02)00372-X [Google Scholar]
  17. Fox, D., Burgard, W., & Thrun, S.
    (1997) The dynamic window approach to collision avoidance. IEEE Robotics Automation Magazine, 4(1), 23–33. 10.1109/100.580977
    https://doi.org/10.1109/100.580977 [Google Scholar]
  18. Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Sax-ton, D., Shanahan, M., … Eslami, S. M. A.
    (2018, 10–15Jul). Conditional Neural Processes. InJ. Dy & A. Krause (Eds.), Proceedings of the 35th international conference on machine learning (Vol.801, pp. 1704–1713). PMLR. Retrieved fromproceedings.mlr.press/v88/garnelo18a.html
    [Google Scholar]
  19. Giesbrecht, J.
    (2004) Global path planning for unmanned ground vehicles (Tech. Rep.). Defence Research and Development Suffield (Alberta).
    [Google Scholar]
  20. Glasius, R., Komoda, A., & Gielen, S. C.
    (1995) Neural network dynamics for path planning and obstacle avoidance. Neural Networks, 8 (1), 125–133. 10.1016/0893‑6080(94)E0045‑M
    https://doi.org/10.1016/0893-6080(94)E0045-M [Google Scholar]
  21. Good fellow, I.
    (2016) Nips 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160.
    [Google Scholar]
  22. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y.
    (2014) Generative adversarial networks. arXiv preprint arXiv:1406.2661.
    [Google Scholar]
  23. Gordon, J., Bruinsma, W. P., Foong, A. Y., Requeima, J., Dubois, Y., & Turner, R. E.
    (2019) Convolutional conditional neural processes. arXiv preprint arXiv:1910.13556.
    [Google Scholar]
  24. Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A.
    (2018) Social gan: Socially acceptable trajectories with generative adversarial networks. InProceedings of the ieee conference on computer vision and pattern recognition (pp. 2255–2264). 10.1109/CVPR.2018.00240
    https://doi.org/10.1109/CVPR.2018.00240 [Google Scholar]
  25. Hall, E.
    (1966) The hidden dimension. New York, NY, US: Anchor Books.
    [Google Scholar]
  26. Helbing, D., & Molnar, P.
    (1995) Social force model for pedestrian dynamics. Physical review E, 51(5), 4282. 10.1103/PhysRevE.51.4282
    https://doi.org/10.1103/PhysRevE.51.4282 [Google Scholar]
  27. Holtz, J., & Biswas, J.
    (2021) Socialgym: A framework for benchmarking social robot navigation. arXiv preprint arXiv:2109.11011.
    [Google Scholar]
  28. Huang, K.-C., Li, J.-Y., & Fu, L.-C.
    (2010) Human-oriented navigation for service providing in home environment. InSice annual conference 2010, proceedings of (pp. 1892–1897). Taipei, Taiwan.
    [Google Scholar]
  29. Kambhampati, S., & Davis, L.
    (1986) Multiresolution path planning for mobile robots. IEEE Journal on Robotics and Automation, 2(3), 135–145. 10.1109/JRA.1986.1087051
    https://doi.org/10.1109/JRA.1986.1087051 [Google Scholar]
  30. Karnan, H., Nair, A., Xiao, X., Warnell, G., Pirk, S., Toshev, A., … Stone, P.
    (2022) Socially compliant navigation dataset (scand): A large-scale dataset of demonstrations for social navigation. arXiv preprint arXiv:2203.15041. 10.1109/LRA.2022.3184025
    https://doi.org/10.1109/LRA.2022.3184025 [Google Scholar]
  31. Khatib, O.
    (1985) Real-time obstacle avoidance for manipulators and mobile robots. InProceedings. 1985 ieee international conference on robotics and automation (Vol.21, p.500–505). 10.1109/ROBOT.1985.1087247
    https://doi.org/10.1109/ROBOT.1985.1087247 [Google Scholar]
  32. Kim, B., & Pineau, J.
    (2016) Socially adaptive path planning in human environments using inverse reinforcement learning. International Journal of Social Robotics, 8(1), 51–66. 10.1007/s12369‑015‑0310‑2
    https://doi.org/10.1007/s12369-015-0310-2 [Google Scholar]
  33. Kitani, K., Ziebart, B., Bagnell, J., & Hebert, M.
    (2012) Activity forecasting. Computer Vision-ECCV 2012, 201–214. 10.1007/978‑3‑642‑33765‑9_15
    https://doi.org/10.1007/978-3-642-33765-9_15 [Google Scholar]
  34. Koren, Y., & Borenstein, J.
    (1991) Potential field methods and their inherent limitations for mobile robot navigation. InProceedings. 1991 ieee international conference on robotics and automation (p.1398–1404vol.21). 10.1109/ROBOT.1991.131810
    https://doi.org/10.1109/ROBOT.1991.131810 [Google Scholar]
  35. Kothari, P., Kreiss, S., & Alahi, A.
    (2021) Human trajectory forecasting in crowds: A deep learning perspective. IEEE Trans-actions on Intelligent Transportation Systems.
    [Google Scholar]
  36. Kretzschmar, H., Spies, M., Sprunk, C., & Burgard, W.
    (2016) Socially compliant mobile robot navigation via inverse reinforcement learning. The International Journal of Robotics Research, 35(11), 1289–1307. 10.1177/0278364915619772
    https://doi.org/10.1177/0278364915619772 [Google Scholar]
  37. Kruse, T., Pandey, A. K., Alami, R., & Kirsch, A.
    (2013) Human-aware robot navigation: A survey. Robotics and Autonomous Systems, 61(12), 1726–1743. 10.1016/j.robot.2013.05.007
    https://doi.org/10.1016/j.robot.2013.05.007 [Google Scholar]
  38. Kuderer, M., Kretzschmar, H., Sprunk, C., & Burgard, W.
    (2012) Feature-based prediction of trajectories for socially compliant navigation. InRobotics: science and systems.
    [Google Scholar]
  39. Lam, C.-P., Chou, C.-T., Chang, C.-F., & Fu, L.-C.
    (2010) Human-centered robot navigation – toward a harmoniously coexisting multi-human and multi-robot environment. InIntelligent robots and systems (iros), 2010 ieee/rsj international conference on (pp. 1813–1818). Taipei, Taiwan.
    [Google Scholar]
  40. Latombe, J.
    (1991) Robot motion planning: Edition en anglais. Springer. Retrieved fromhttps://books.google.com.tr/books?id=Mbo_p4-46-cC. 10.1007/978‑1‑4615‑4022‑9
    https://doi.org/10.1007/978-1-4615-4022-9 [Google Scholar]
  41. Lerner, A., Chrysanthou, Y., & Lischinski, D.
    (2007) Crowds by example. InComputer graphics forum (Vol.261, pp. 655–664). 10.1111/j.1467‑8659.2007.01089.x
    https://doi.org/10.1111/j.1467-8659.2007.01089.x [Google Scholar]
  42. Levine, S., Popovic, Z., & Koltun, V.
    (2011) Nonlinear inverse reinforcement learning with gaussian processes. Advances in neural information processing systems, 241, 19–27.
    [Google Scholar]
  43. Manso, L. J., Nunez, P., Calderita, L. V., Faria, D. R., & Bachiller, P.
    (2020) Socnav1: A dataset to benchmark and learn social navigation conventions. Data, 5(1), 7. 10.3390/data5010007
    https://doi.org/10.3390/data5010007 [Google Scholar]
  44. Martin-Martin, R., Patel, M., Rezatofighi, H., Shenoi, A., Gwak, J., Frankel, E., … Savarese, S.
    (2021) Jrdb: A dataset and benchmark of egocentric robot visual perception of humans in built environments. IEEE transactions on pattern analysis and machine intelligence. 10.1109/TPAMI.2021.3070543
    https://doi.org/10.1109/TPAMI.2021.3070543 [Google Scholar]
  45. Mavrogiannis, C., Alves-Oliveira, P., Thomason, W., & Knepper, R. A.
    (2022) Social momentum: Design and evaluation of a framework for socially competent robot navigation. ACM Transactions on Human-Robot Interaction (THRI), 11(2), 137. 10.1145/3495244
    https://doi.org/10.1145/3495244 [Google Scholar]
  46. Mavrogiannis, C., Baldini, F., Wang, A., Zhao, D., Trautman, P., Steinfeld, A., & Oh, J.
    (2021) Core challenges of social robot navigation: A survey. arXiv preprint arXiv:2103.05668.
    [Google Scholar]
  47. Mead, R., Atrash, A., & Matarié, M. J.
    (2011) Proxemic feature recognition for interactive robots: Automating metrics from the social sciences. InSocial robotics (pp. 52–61). Springer. 10.1007/978‑3‑642‑25504‑5_6
    https://doi.org/10.1007/978-3-642-25504-5_6 [Google Scholar]
  48. Murphy, R. R.
    (2019) Introduction to ai robotics. MIT press.
    [Google Scholar]
  49. Nonaka, S., Inoue, K., Arai, T., & Mae, Y.
    (2004) Evaluation of human sense of security for coexisting robots using virtual reality. 1st report: evaluation of pick and place motion of hu-manoid robots. InIeee international conference on robotics and automation, 2004. proceedings. icra’04. 2(004 (Vol.31, pp. 2770–2775). 10.1109/ROBOT.2004.1307480
    https://doi.org/10.1109/ROBOT.2004.1307480 [Google Scholar]
  50. Nourbakhsh, I., Kunz, C., & Willeke, T.
    (2003) The mobot museum robot installations: a five year experiment. InProceedings 2003 ieee/rsj international conference on intelligent robots and systems (iros 2003) (cat. no.03ch37453) (Vol.41, p.3636–3641vol.31). 10.1109/IROS.2003.1249720
    https://doi.org/10.1109/IROS.2003.1249720 [Google Scholar]
  51. Okal, B., & Arras, K. O.
    (2016) Formalizing normative robot behavior. InInternational conference on social robotics (pp. 62–71). 10.1007/978‑3‑319‑47437‑3_7
    https://doi.org/10.1007/978-3-319-47437-3_7 [Google Scholar]
  52. Orebäck, A., & Christensen, H. I.
    (2003) Evaluation of architectures for mobile robotics. Autonomous robots, 14(1), 33–49. 10.1023/A:1020975419546
    https://doi.org/10.1023/A:1020975419546 [Google Scholar]
  53. Pacchierotti, E., Christensen, H. I., & Jensfelt, P.
    (2006) Evaluation of passing distance for social robots. InRoman 2006-the 15th ieee international symposium on robot and human interactive communication (pp. 315–320). 10.1109/ROMAN.2006.314436
    https://doi.org/10.1109/ROMAN.2006.314436 [Google Scholar]
  54. Pellegrini, S., Ess, A., Schindler, K., & Van Gool, L.
    (2009) You’ll never walk alone: Modeling social behavior for multi-target tracking. In2009 ieee 12th international conference on computer vision (pp. 261–268). 10.1109/ICCV.2009.5459260
    https://doi.org/10.1109/ICCV.2009.5459260 [Google Scholar]
  55. Pérez-Higueras, N., Caballero, F., & Merino, L.
    (2018) Learning human-aware path planning with fully convolutional networks. In2018 ieee international conference on robotics and automation (iera) (pp. 5897–5902). 10.1109/ICRA.2018.8460851
    https://doi.org/10.1109/ICRA.2018.8460851 [Google Scholar]
  56. Quinlan, S., & Khatib, O.
    (1993) Elastic bands: Connecting path planning and control. In[1993] proceedings ieee international conference on robotics and automation (pp. 802–807). 10.1109/ROBOT.1993.291936
    https://doi.org/10.1109/ROBOT.1993.291936 [Google Scholar]
  57. Rohmer, E., Singh, S. P. N., & Freese, M.
    (2013) Cop-peliaSim (formerly V-REP): a Versatile and Scalable Robot Simulation Framework. InProc. of the international conference on intelligent robots and systems (iros). (www.coppeliarobotics.com)
    [Google Scholar]
  58. Rosmann, C., Hoffmann, F., & Bertram, T.
    (2015) Timed-Elastic-Bands for time-optimal point-to-point nonlinear model predictive control. In2015 european control conference (ecc) (p.3352–3357). 10.1109/ECC.2015.7331052
    https://doi.org/10.1109/ECC.2015.7331052 [Google Scholar]
  59. Shorten, C., & Khoshgoftaar, T. M.
    (2019) A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1–48. 10.1186/s40537‑019‑0197‑0
    https://doi.org/10.1186/s40537-019-0197-0 [Google Scholar]
  60. Stein, P., Spalanzani, A., Santos, V., & Laugier, C.
    (2016) Leader following: A study on classification and selection. Robotics and Autonomous Systems, 751, 79–95. 10.1016/j.robot.2014.09.028
    https://doi.org/10.1016/j.robot.2014.09.028 [Google Scholar]
  61. Syrdal, D. S., Koay, K. L., Walters, M. L., & Dautenhahn, K.
    (2007) A personalized robot companion-the role of individual differences on spatial preferences in hri scenarios. InRobot and human interactive communication, 2007. ro-man 2007. the 16th ieee international symposium on (pp. 1143–1148). Jeju Island, Korea. 10.1109/ROMAN.2007.4415252
    https://doi.org/10.1109/ROMAN.2007.4415252 [Google Scholar]
  62. Tai, L., Zhang, J., Liu, M., & Burgard, W.
    (2018) Socially compliant navigation through raw depth inputs with generative adversarial imitation learning. In2018 ieee international conference on robotics and automation (icra) (pp. 1111–1117). 10.1109/ICRA.2018.8460968
    https://doi.org/10.1109/ICRA.2018.8460968 [Google Scholar]
  63. Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A. B., Dellaert, F.
    , … others (2000) Probabilistic algorithms and the interactive museum tour-guide robot minerva. The International Journal of Robotics Research, 19(11), 972–999. 10.1177/02783640022067922
    https://doi.org/10.1177/02783640022067922 [Google Scholar]
  64. Tipaldi, G. D., & Arras, K. O.
    (2011) Please do not disturb! minimum interference coverage for social robots. In2011 ieee/rsj international conference on intelligent robots and systems (pp. 1968–1973).
    [Google Scholar]
  65. Trautman, P., & Krause, A.
    (2010) Unfreezing the robot: Navigation in dense, interacting crowds. In2010 ieee/rsj international conference on intelligent robots and systems (pp. 797–803). 10.1109/IROS.2010.5654369
    https://doi.org/10.1109/IROS.2010.5654369 [Google Scholar]
  66. Tsoi, N., Hussein, M., Espinoza, J., Ruiz, X., & Vázquez, M.
    (2020) Sean: Social environment for autonomous navigation. InProceedings of the 8th international conference on human-agent interaction (pp. 281–283). 10.1145/3406499.3418760
    https://doi.org/10.1145/3406499.3418760 [Google Scholar]
  67. Vadakkepat, P., Tan, K. C., & Ming-Liang, W.
    (2000) Evolutionary artificial potential fields and their application in real time robot path planning. InProceedings of the 2000 congress on evolutionary computation. cec 00 (cat. no. 00th8512) (Vol.11, pp. 256–263). 10.1109/CEC.2000.870304
    https://doi.org/10.1109/CEC.2000.870304 [Google Scholar]
  68. Van den Berg, J., Lin, M., & Manocha, D.
    (2008) Reciprocal velocity obstacles for real-time multi-agent navigation. In2008 ieee international conference on robotics and automation (pp. 1928–1935). 10.1109/ROBOT.2008.4543489
    https://doi.org/10.1109/ROBOT.2008.4543489 [Google Scholar]
  69. Vasquez, D., Okal, B., & Arras, K. O.
    (2014) Inverse reinforcement learning algorithms and features for robot navigation in crowds: an experimental comparison. In2014 ieee/rsj international conference on intelligent robots and systems (pp. 1341–1346). 10.1109/IROS.2014.6942731
    https://doi.org/10.1109/IROS.2014.6942731 [Google Scholar]
  70. Vemula, A., Muelling, K., & Oh, J.
    (2018) Social attention: Modeling attention in human crowds. In2018 ieee international conference on robotics and automation (icra) (pp. 4601–4607). 10.1109/ICRA.2018.8460504
    https://doi.org/10.1109/ICRA.2018.8460504 [Google Scholar]
  71. Wulfmeier, M., Ondruska, P., & Posner, I.
    (2015) Maximum entropy deep inverse reinforcement learning. arXiv preprint arXiv:1507.04888.
    [Google Scholar]
  72. Yan, Z., Duckett, T., & Bellotto, N.
    (2017, September). Online learning for human classification in 3d lidar-based tracking. InIn proceedings of the 2017 ieee/rsj international conference on intelligent robots and systems (iros). Vancouver, Canada. 10.1109/IROS.2017.8202247
    https://doi.org/10.1109/IROS.2017.8202247 [Google Scholar]
  73. Yi, S., Li, H., & Wang, X.
    (2015) Understanding pedestrian behaviors from stationary crowd groups. InProceedings of the ieee conference on computer vision and pattern recognition (pp. 3488–3496). 10.1109/CVPR.2015.7298971
    https://doi.org/10.1109/CVPR.2015.7298971 [Google Scholar]
  74. Zanlungo, F., Ikeda, T., & Kanda, T.
    (2011) Social force model with explicit collision prediction. EPL(Europhysics Letters), 93(6), 68005. 10.1209/0295‑5075/93/68005
    https://doi.org/10.1209/0295-5075/93/68005 [Google Scholar]
  75. Zhu, Q., Yan, Y., & Xing, Z.
    (2006) Robot path planning based on artificial potential field approach with simulated annealing. InSixth international conference on intelligent systems design and applications (Vol.21, pp. 622–627). 10.1109/ISDA.2006.253908
    https://doi.org/10.1109/ISDA.2006.253908 [Google Scholar]

Data & Media loading...

Most Cited

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error