ABSTRACT
Most motion in robotics is purely functional, planned to achieve the goal and avoid collisions. Such motion is great in isolation, but collaboration affords a human who is watching the motion and making inferences about it, trying to coordinate with the robot to achieve the task. This paper analyzes the benefit of planning motion that explicitly enables the collaborator's inferences on the success of physical collaboration, as measured by both objective and subjective metrics. Results suggest that legible motion, planned to clearly express the robot's intent, leads to more fluent collaborations than predictable motion, planned to match the collaborator's expectations. Furthermore, purely functional motion can harm coordination, which negatively affects both task efficiency, as well as the participants' perception of the collaboration.
- R. Alami, A. Albu-Schaeffer, A. Bicchi, R. Bischoff, R. Chatila, A. D. Luca, A. D. Santis, G. Giralt, J. Guiochet, G. Hirzinger, F. Ingrand, V. Lippiello, R. Mattone, D. Powell, S. Sen, B. Siciliano, G. Tonietti, and L. Villani. Safe and Dependable Physical Human-Robot Interaction in Anthropic Domains: State of the Art and Challenges. In IROS Workshop on pHRI, 2006.Google Scholar
- R. Alami, A. Clodic, V. Montreuil, E. A. Sisbot, and R. Chatila. Task planning for human-robot interaction. In Smart objects and ambient intelligence, 2005. Google ScholarDigital Library
- G. Csibra and G. Gergely. Obsessed with goals: Functions and mechanisms of teleological interpretation of actions in humans. Acta Psychologica, 124(1):60 -- 78, 2007.Google ScholarCross Ref
- A. Dragan and S. Srinivasa. Generating legible motion. In RSS, 2013.Google ScholarCross Ref
- A. Dragan and S. Srinivasa. Familiarization to robot motion. In HRI, 2014. Google ScholarDigital Library
- A. D. Dragan, K. C. Lee, and S. S. Srinivasa. Legibility and predictability of robot motion. In HRI, 2013. Google ScholarDigital Library
- G. Gergely, Z. Nadasdy, G. Csibra, and S. Biro. Taking the intentional stance at 12 months of age. Cognition, 56(2):165 -- 193, 1995.Google ScholarCross Ref
- M. J. Gielniak and A. L. Thomaz. Generating anticipation in robot motion. In RO-MAN, 2011.Google ScholarCross Ref
- G. Hoffman. Evaluating fluency in human-robot collaboration. In HRI Workshop on Human Robot Collaboration, 2013.Google Scholar
- T. S. Jim Mainprice, E. Akin Sisbot and R. Alami. Planning safe and legible hand-over motions for human-robot interaction. In IARP Workshop on Technical Challenges for Dependable Robots in Human Environments, 2010.Google Scholar
- J. J. Kuffner and S. M. LaValle. Rrt-connect: An efficient approach to single-query path planning. In ICRA, 2000.Google ScholarCross Ref
- J.-C. Latombe. Robot Motion Planning. Kluwer Academic Publishers, Norwell, MA, USA, 1991. Google ScholarCross Ref
- M. K. Lee, S. Kiesler, J. Forlizzi, S. Srinivasa, and P. Rybski. Gracefully mitigating breakdowns in robotic services. In HRI, 2010. Google ScholarDigital Library
- C. Lichtenthäler, T. Lorenz, and A. Kirsch. Towards a legibility metric: How to measure the perceived value of a robot. In ICSR Work-In-Progress-Track, 2011.Google Scholar
- M. A. Marks, M. J. Sabella, C. S. Burke, and S. J. Zaccaro. The impact of cross-training on team effectiveness. Journal of Applied Psychology, 87(1):3, 2002.Google ScholarCross Ref
- B. Mutlu, J. Forlizzi, and J. Hodgins. A storytelling robot: Modeling and evaluation of human-like gaze behavior. In Humanoid Robots, 2006.Google Scholar
- I. Sucan, M. Moll, and L. Kavraki. The open motion planning library. Robotics & Automation Magazine, IEEE, 2012.Google Scholar
- L. Takayama, D. Dooley, and W. Ju. Expressing thought: improving robot readability with animation principles. In HRI, 2011. Google ScholarDigital Library
- M. Tomasello, M. Carptenter, J. Call, T. Behne, and H. Moll. Understanding and sharing intentions: the origins of cultural cognition. Behavioral and Brain Sciences, 2004.Google Scholar
- C. Vesper, S. Butterfill, G. Knoblich, and N. Sebanz. A minimal architecture for joint action. Neural Networks, 23(8):998--1003, 2010. Google ScholarDigital Library
- M. Zucker, N. Ratliff, A. Dragan, M. Pivtoraiko, M. Klingensmith, C. Dellin, J. Bagnell, and S. Srinivasa. Covariant hamiltonian optimization for motion planning. IJRR, 2013. Google ScholarDigital Library
Index Terms
- Effects of Robot Motion on Human-Robot Collaboration
Recommendations
Concurrent Probabilistic Motion Primitives for Obstacle Avoidance and Human-Robot Collaboration
Intelligent Robotics and ApplicationsAbstractThe paper proposed a new method to endow a robot with the ability of human-robot collaboration and online obstacle avoidance simultaneously. In other words, we construct a probabilistic model for human-robot collaboration primitives to learn the ...
Toward an argumentation-based dialogue framework for human-robot collaboration
ICMI '12: Proceedings of the 14th ACM international conference on Multimodal interactionThe research on human-robot dialogue to support fluent human robot interaction is still in its early stages. Current issues in the human-robot dialogue domain could be divided into two major categories, which are described in this proposal as the "what ...
Human-Robot Collaboration: an analysis of worker’s performance
AbstractCollaborative robots are an important enabling technology of Industry 4.0. The interaction between humans and robots, called Human-Robot Collaboration (HRC), aims to improve system performance. However, it is necessary to investigate in depth the ...
Comments