Skip to main content
SpringerLink
Log in

Integrating human observer inferences into robot motion planning

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Our goal is to enable robots to produce motion that is suitable for human–robot collaboration and co-existence. Most motion in robotics is purely functional, ideal when the robot is performing a task in isolation. In collaboration, however, the robot’s motion has an observer, watching and interpreting the motion. In this work, we move beyond functional motion, and introduce the notion of an observer into motion planning, so that robots can generate motion that is mindful of how it will be interpreted by a human collaborator. We formalize predictability and legibility as properties of motion that naturally arise from the inferences in opposing directions that the observer makes, drawing on action interpretation theory in psychology. We propose models for these inferences based on the principle of rational action, and derive constrained functional trajectory optimization techniques for planning motion that is predictable or legible. Finally, we present experiments that test our work on novice users, and discuss the remaining challenges in enabling robots to generate such motion online in complex situations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. In ICML.

  • Alami, R., Albu-Schaeffer, A., Bicchi, A., Bischoff, R., Chatila, R., Luca, A. D., Santis, A. D., Giralt, G., Guiochet, J., Hirzinger, G., Ingrand, F., Lippiello, V., Mattone, R., Powell, D., Sen, S., Siciliano, B., Tonietti, G., & Villani, L. (2006). Safe and dependable physical human–robot interaction in anthropic domains: State of the art and challenges. In IROS workshop on pHRI.

  • Alami, R., Clodic, A., Montreuil, V., Sisbot, E. A., & Chatila, R. (2006). Toward human-aware robot task planning. In AAAI spring symposium, (pp. 39–46).

  • Argall, B., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5), 469–483.

    Article  Google Scholar 

  • Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009). Action understanding as inverse planning appendix. Cognition, 113, 329.

    Article  Google Scholar 

  • Beetz, M., Stulp, F., Esden-Tempski, P., Fedrizzi, A., Klank, U., Kresse, I., et al. (2010). Generality and legibility in mobile manipulation. Autonomous Robots, 28, 21–44.

    Article  Google Scholar 

  • Bergersen, G., Hannay, J., Sjoberg, D., Dyba, T., & Karahasanovic, A. (2011). Combining time and quality: Inferring skill from tests of programming performance. In ESEM.

  • Boyan, J., & Moore, A. (1995). Generalization in reinforcement learning: Safely approximating the value function. NIPS. Cambridge, MA: MIT.

    Google Scholar 

  • Brock, O., & Khatib, O. (2002). Elastic strips: A framework for motion generation in human environments. International Journal of Robotics Research, 21(12), 1031.

    Article  Google Scholar 

  • Carter, E. J., Hodgins, J. K., & Rakison, D. H. (2011). Exploring the neural correlates of goal-directed action and intention understanding. NeuroImage, 54(2), 1634–1642.

    Article  Google Scholar 

  • Csibra, G., & Gergely, G. (1998). The teleological origins of mentalistic action explanations: A developmental hypothesis. Developmental Science, 1, 255–259.

    Article  Google Scholar 

  • Csibra, G., & Gergely, G. (2007). Obsessed with goals: Functions and mechanisms of teleological interpretation of actions in humans. Acta Psychologica, 124(1), 60–78.

    Article  Google Scholar 

  • Dey, D., Liu, T. Y., Hebert, M., & Bagnell, J. A. (2012, July). Contextual sequence prediction with application to control library optimization. In R: SS.

  • Dragan, A., Gordon, G, & Srinivasa, S. (2011). Setting the right goals: Learning from experience in manipulation planning. In ISRR.

  • Dragan, A., Lee, K., & Srinivasa, S. (2013). Legibility and predictability of robot motion. In Human–Robot Interaction.

  • Dragan, A., Lee, K., & Srinivasa, S. (2014). Familiarization to robot motion. In Human–Robot Interaction.

  • Dragan, A., Ratliff, N., & Srinivasa, S. (2011, May). Manipulation planning with goal sets using constrained trajectory optimization. In ICRA.

  • Dragan, A., & Srinivasa, S. (2013). Generating legible motion. In Robotics: Science and systems.

  • Dragan, A., & Srinivasa, S. S. (2012). Formalizing assistive teleoperation. In R: SS.

  • Holladay, R., Dragan, A., & Srinivasa, S. S. (2014). Legible robot pointing. In RO-MAN.

  • Fan, J., He, J., & Tillery, S. (2006). Control of hand orientation and arm movement during reach and grasp. Experimental Brain Research, 171, 283–296.

    Article  Google Scholar 

  • Flah, T., & Hogan, N. (1985, July). The coordination of arm movements: An experimentally confirmed mathematical model. The Journal of Neuroscience, 5, 1688–1703.

  • Gergely, G., Nadasdy, Z., Csibra, G., & Biro, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56(2), 165–193.

    Article  Google Scholar 

  • Gielniak, M. & Thomaz, A. (2011). Generating anticipation in robot motion. In RO-MAN.

  • Gielniak, M. & Thomaz, A. L. (2011). Spatiotemporal correspondence as a metric for human-like robot motion. In ACM/IEEE HRI.

  • Hauf, P., & Prinz, W. (2005). The understanding of own and others actions during infancy: You-like-me or me-like-you? Interaction Studies, 6(3), 429–445.

    Article  Google Scholar 

  • Heinzmann, J., & Zelinsky, A. (1999). The safe control of human-friendly robots. In IEEE/RSJ IROS.

  • Igel, C., Toussaint, M., & Weishui, W. (2006). Rprop using the natural gradient. Trends and applications in constructive approximation (pp. 259–272). Berlin: Springer.

    Google Scholar 

  • Jim Mainprice, T. S., Akin Sisbot, E., & Alami, R. (2010). Planning safe and legible hand-over motions for human–robot interaction. In IARP workshop on technical challenges for dependable robots in human environments.

  • Jain, A., Wojcik, B., Joachims, T., & Saxena, A. (2013). Learning trajectory preferences for manipulators via iterative improvement. In NIPS.

  • Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., & Schaal, S. (2011). STOMP: Stochastic trajectory optimization for motion planning. In IEEE ICRA.

  • Kalakrishnan, M., Pastor, P., Righetti, L., & Schaal, S. (2013). Learning objective functions for manipulation. In IEEE ICRA.

  • Kamewari, K., Kato, M., Kanda, T., Ishiguro, H., & Hiraki, K. (2005). Six-and-a-half-month-old children positively attribute goals to human action and to humanoid–robot motion. Cognitive Development, 20(2), 303–320.

    Article  Google Scholar 

  • Kruse, T., Basili, P., Glasauer, S., & Kirsch, A. (2012). Legible robot navigation in the proximity of moving humans. In Advanced robotics and its social impacts (ARSO).

  • Lacquaniti, F., & Soechting, J. (1982). Coordination of arm and wrist motion during a reaching task. The Journal of Neuroscience, 2, 399–408.

    Google Scholar 

  • Lasseter, J. (1987). Principles of traditional animation applied to 3d computer animation. In SIGGRAPH.

  • 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.

  • Lichtenthäler, C., Lorenz, T., & Kirsch, A. (2011). Towards a legibility metric: How to measure the perceived value of a robot. In ICSR work-in-progress-track, 2011.

  • Lichtenthäler, C., & Kirsch, A. (2013). Towards legible robot navigation—How to increase the intend expressiveness of robot navigation behavior. In International conference on social robotics—workshop embodied communication of goals and intentions.

  • Nikolaidis, S., & Shah, J. (2012) Human–robot teaming using shared mental models. In ACM/IEEE HRI.

  • Phillips, A. T., & Wellman, H. M. (2005). Infants’ understanding of object-directed action. Cognition, 98(2), 137–155.

    Article  Google Scholar 

  • Quinlan, S. (1994) The real-time modification of collision-free paths. Ph.D. thesis, Stanford University.

  • Ratliff, N., Bagnell, J. A., & Zinkevich, M. (2006) Maximum margin planning. In ICML.

  • N. Ratliff, M. Zucker, J. A. D. Bagnell, & S. Srinivasa. (2009, MAy). Chomp: Gradient optimization techniques for efficient motion planning. In ICRA.

  • Short, E., Hart, J., Vu, M., & Scassellati, B. (2010). No fair!! an interaction with a cheating robot. In ACM/IEEE HRI.

  • Sodian, B., & Thoermer, C. (2004). Infants’ understanding of looking, pointing, and reaching as cues to goal-directed action. Journal of Cognition and Development, 5(3), 289–316.

    Article  Google Scholar 

  • Srinivasa, S., Berenson, D., Cakmak, M., Collet, A., Dogar, M., Dragan, A., Knepper, R., Niemueller, T., Strabala, K., Weghe, M. V., & Ziegler, J. (2012). Herb 2.0: Lessons learned from developing a mobile manipulator for the home. In Proceedings of the IEEE, special issue on quality of life technology.

  • Szafir, D., Mutlu, B., & Fong, T. (2014). Communication of intent in assistive free flyers. In HRI.

  • Takayama, L., Dooley, D., & Ju, W. (2011). Expressing thought: Improving robot readability with animation principles. In HRI.

  • Tellex, S., Knepper, R., Li, A., Rus, D., & Roy, N. (2014) Asking for Help Using Inverse Semantics. In RSS.

  • Thomas, F., & Johnston, O. (1981). Disney animation. In F. Johnston (Ed.), The illusion of life. New York: Hyperion.

    Google Scholar 

  • Tinker, M. A. (1963). Legibility of print. Ames: Iowa State University Press.

    Google Scholar 

  • Todorov, E. & Li, W. (2005). A generalized iterative lqg method for locally-optimal feedback control of constrained nonlinear stochastic systems. In ACC.

  • Tomasello, M., Carptenter, M., Call, J., Behne, T., & Moll, H. (2004). Understanding and sharing intentions: the origins of cultural cognition. Behavioral and Brain Sciences, 28, 675.

    Google Scholar 

  • Toussaint, M. (2009). Robot trajectory optimization using approximate inference. In International conference on machine learning.

  • Vesper, C., Butterfill, S., Knoblich, G., & Sebanz, N. (2010). A minimal architecture for joint action. Neural Networks, 23, 998.

    Article  Google Scholar 

  • Witkin, A. & Kass, M. (1988). Spacetime constraints. In SIGGRAPH.

  • Woodward, A. L. (1998). Infants selectively encode the goal object of an actor’s reach. Cognition, 69(1), 1–34.

    Article  Google Scholar 

  • Wiese, E., Wykowska, A., Zwickel, J., & Müller, H. (2012). I see what you mean: how attentional selection is shaped by ascribing intentions to others. PLoS One, 7, e45391.

    Article  Google Scholar 

  • Ziebart, B. D., Maas, A., Bagnell, J. A., & Dey, A. (2008). Maximum entropy inverse reinforcement learning. In AAAI.

  • Zucker, M., Ratliff, N., Dragan, A., Pivtoraiko, M., Klingensmith, M., Klingensmith, M., et al. (2013). Covariant Hamiltonian optimization for motion planning. International Journal of Robotics Researc, 32, 1164.

    Article  Google Scholar 

Download references

Acknowledgments

We thank Geoff Gordon, Jodi Forlizzi, Hendrik Christiansen, Kenton Lee, Chris Dellin, Alberto Rodriguez, and the members of the Personal Robotics Lab for fruitful discussion and advice. This material is based upon work supported by NSF-IIS-0916557, NSF-EEC-0540865, ONR-YIP 2012, the Intel Embedded Computing ISTC, and the Intel Ph.D. Fellowship.

Author information

Authors and Affiliations

  1. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA

    Anca Dragan & Siddhartha Srinivasa

Authors
  1. Anca Dragan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siddhartha Srinivasa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anca Dragan.

Additional information

This paper combines work from Dragan et al. (2013), and Dragan and Srinivasa (2013).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dragan, A., Srinivasa, S. Integrating human observer inferences into robot motion planning. Auton Robot 37, 351–368 (2014). https://doi.org/10.1007/s10514-014-9408-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-014-9408-x

Keywords

Search

Navigation