Kyle Strabala
Min Kyung Lee
Abstract
A handover is a complex collaboration, where actors coordinate in time and space to transfer control of an object. This coordination comprises two processes: the physical process of moving to get close enough to transfer the object, and the cognitive process of exchanging information to guide the transfer. Despite this complexity, we humans are capable of performing handovers seamlessly in a wide variety of situations, even when unexpected. This suggests a common procedure that guides all handover interactions. Our goal is to codify that procedure.
To that end, we first study how people hand over objects to each other in order to understand their coordination process and the signals and cues that they use and observe with their partners. Based on these studies, we propose a coordination structure for human-robot handovers that considers the physical and social-cognitive aspects of the interaction separately. This handover structure describes how people approach, reach out their hands, and transfer objects while simultaneously coordinating the what, when, and where of handovers: to agree that the handover will happen (and with what object), to establish the timing of the handover, and to decide the configuration at which the handover will occur. We experimentally evaluate human-robot handover behaviors that exploit this structure and offer design implications for seamless human-robot handover interactions.
- Agah, A., & Tanie, K. (1997). Human interaction with a service robot: Mobile-manipulator handing over an object to a human. In Robotics and automation (pp. 575--580).Google Scholar
- Aleotti, J., Micelli, V., & Caselli, S. (2012). Comfortable robot to human object hand-over. In Robot and human interactive communication (pp. 771--776).Google Scholar
- Basili, P., Huber, M., Brandt, T., Hirche, S., & Glasauer, S. (2009). Investigating human-human approach and hand-over. In Human centered robot systems: Cognition, interaction, technology (Vol. 6, pp. 151--160). Berlin, Heidelberg: Springer.Google Scholar
- Becchio, C., Sartori, L., & Castiello, U. (2010). Toward you: The social side of actions. Current Directions in Psychological Science, 19(3), 183--188Google ScholarCross Ref
- Cakmak, M., Srinivasa, S., Lee, M. K., Forlizzi, J., & Kiesler, S. (2011). Human preferences for robot-human hand-over configurations. In Intelligent robots and system (pp. 1986--1993).Google Scholar
- Cakmak, M., Srinivasa, S., Lee, M. K., Kiesler, S., & Forlizzi, J. (2011). Using spatial and temporal contrast for fluent robot-human hand-overs. In Human-robot interaction (pp. 489--496). Google ScholarDigital Library
- Castiello, U. (2003). Understanding other people's actions: Intention and attention. Journal of Experimental Psychology: Human Perception and Performance, 29(2), 416--430.Google ScholarCross Ref
- Chakraborty, B. (2007). Feature selection and classification techniques for multivariate time series. Innovative Computing, Information and Control, 42 Google ScholarDigital Library
- Chan, W. P., Parker, C. A., Loos, H. M. Van der, & Croft, E. A. (2012). Grip forces and load forces in handovers: implications for designing human-robot handover controllers. In Human-robot interaction (pp. 9--16). Google ScholarDigital Library
- Clark, H. H. (1996). Using language. Cambridge University Press.Google Scholar
- Edsinger, A., & Kemp, C. (2007). Human-robot interaction for cooperative manipulation: Handing objects to one another. In Robot and human interactive communication (pp. 1167--1172).Google Scholar
- Flash, T., & Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. Journal of Neuroscience, 5(7), 1688--1703.Google ScholarCross Ref
- Glasauer, S., Huber, M., Basili, P., Knoll, A., & Brandt, T. (2010). Interacting in time and space: Investigating human-human and human-robot joint action. In Robot and human interactive communication (pp. 252--257).Google Scholar
- Goffman, E. (1959). The presentation of self in everyday life. Garden City, N.Y.: Doubleday.Google Scholar
- Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157--1182. Google ScholarDigital Library
- Hoffman, G., & Breazeal, C. (2007). Cost-based anticipatory action selection for human-robot fluency. In Robotics (Vol. 23, pp. 952--961). Google ScholarDigital Library
- Huber, M., Radrich, H., Wendt, C., Rickert, M., Knoll, A., Brandt, T., et al. (2009). Evaluation of a novel biologically inspired trajectory generator in human-robot interaction. In Robot and human interactive communication (pp. 639--644).Google Scholar
- Huber, M., Rickert, M., Knoll, A., Brandt, T., & Glasauer, S. (2008). Human-robot interaction in handing-over tasks. In Robot and human interactive communication (pp. 107--112).Google Scholar
- Kajikawa, S., & Ishikawa, E. (2000). Trajectory planning for hand-over between human and robot. In Robot and human interactive communication (pp. 281--287).Google Scholar
- Kajikawa, S., Okino, T., Ohba, K., & Inooka, H. (1995). Motion planning for hand-over between human and robot. In Intelligent robots and systems (pp. 193--199). Google ScholarDigital Library
- Kim, J., Park, J., Hwang, Y. K., & Lee, M. (2004). Advanced Grasp Planning for Handover Operation Between Human and Robot: Three Handover Methods in Esteem Etiquettes Using Dual Arms and Hands of Home-Service Robot. In Autonomous robots and agents (pp. 34--39).Google Scholar
- Koay, K., Sisbot, E., Syrdal, D., Walters, M., Dautenhahn, K., & Alami, R. (2007). Exploratory study of a robot approaching a person in the context of handing over an object. In Multidisciplinary collaboration for socially assistive robotics (pp. 18--24).Google Scholar
- Lee, M. K., Forlizzi, J., Kiesler, S., Cakmak, M., & Srinivasa, S. (2011). Predictability or adaptivity? Designing robot handoffs modeled from trained dogs and people. In Human-robot interaction (pp. 179--180). Google ScholarDigital Library
- Lopez-Damian, E., Sidobre, D., DeLaTour, S., & Alami, R. (2006). Grasp planning for interactive object manipulation. In Robotics and automation.Google Scholar
- Mainprice, J., Gharbi, M., Simeon, T., & Alami, R. (2012). Sharing effort in planning human-robot handover tasks. In Robot and human interactive communication (pp. 764--770).Google Scholar
- Micelli, V., Strabala, K. W., & Srinivasa, S. (2011). Perception and control challenges for effective human-robot handoffs. In Robotics: Science and systems workshop on rgb-d cameras.Google Scholar
- Mitchell, T. (1997). Machine learning. McGraw-Hill. Google ScholarDigital Library
- Mumm, J., & Mutlu, B. (2011). Human-robot proxemics: physical and psychological distancing in human-robot interaction. In Human-robot interaction (pp. 331--338). Google ScholarDigital Library
- Mutlu, B., Yamaoka, F., Kanda, T., Ishiguro, H., & Hagita, N. (2009). Nonverbal leakage in robots: communication of intentions through seemingly unintentional behavior. In Human-robot interaction (pp. 69--76). Google ScholarDigital Library
- Nagata, K., Oosaki, Y., Kakikura, M., & Tsukune, H. (1998). Delivery by hand between human and robot based on fingertip force-torque information. In Intelligent robots and systems (Vol. 2, pp. 750--757).Google Scholar
- Pandey, A. K., Ali, M., Warnier, M., & Alami, R. (2011). Towards multi-state visuo-spatial reasoning based proactive human-robot interaction. In Advanced robotics (pp. 143--149).Google Scholar
- Sadigh, M., & Ahmadi, H. (2009). Safe grasping with multi-link fingers based on force sensing. In Robotics and biomimetics (pp. 1796--1802). Google ScholarDigital Library
- Satake, S., Kanda, T., Glas, D., Imai, M., Ishiguro, H., & Hagita, N. (2009). How to approach humans? Strategies for social robots to initiate interaction. In Human-robot interaction (pp. 109--116). Google ScholarDigital Library
- Sebanz, N., Bekkering, H., & Knoblich, G. (2006). Joint action: bodies and minds moving together. Trends in Cognitive Sciences, 10(2), 70--76Google ScholarCross Ref
- Shibata, S., Tanaka, K., & Shimizu, A. (1995). Experimental analysis of handing over. In Robot and human communication (pp. 53--58).Google Scholar
- Sisbot, E., & Alami, R. (2012). A human-aware manipulation planner. In Robotics (pp. 1--13). Google ScholarDigital Library
- Sisbot, E., Alami, R., Simeon, T., Dautenhahn, K., Walters, M., & Woods, S. (2005). Navigation in the presence of humans. In Humanoid robots (pp. 181--188).Google Scholar
- Srinivasa, S. S., Berenson, D., Cakmak, M., Collet, A., Dogar, M. R., Dragan, A. D., et al. (2012). HERB 2.0: Lessons Learned From Developing a Mobile Manipulator for the Home. In Proceedings of the IEEE (Vol. 100, pp. 2410--2428).Google ScholarCross Ref
- Strabala, K., Lee, M. K., Dragan, A., Forlizzi, J., & Srinivasa, S. (2012). Learning the communication of intent prior to physical collaboration. In Robot and human interactive communication (pp. 968--973).Google Scholar
- Takayama, L., & Pantofaru, C. (2009). Influences on proxemic behaviors in human-robot interaction. In Intelligent robots and systems (pp. 5495--5502). Google ScholarDigital Library
Index Terms
- Toward seamless human-robot handovers
Recommendations
Towards seamless inter-technology handovers in vehicular IPv6 communications
Network mobility plays an important role in communications when using different access networks while maintaining application sessions. This is the case of vehicular networks used by the emerging Cooperative Intelligent Transport System (C-ITS), where ...
Fast and Secure Reauthentications for 3GPP Subscribers during WiMAX-WLAN Handovers
Wireless technologies such as the Wireless Local Area Network (WLAN), the Worldwide Interoperability for Microwave Access (WiMAX), and the Third-Generation (3G) mobile communications system complement each other to support a variety of services suited ...
Design and implementation of ARIP for efficient handovers
Part 2: Next Generation Networks (NGNs)Mobile IPv6 (MIPv6), which is a protocol being standardized by Internet Engineering Task Force for global IP mobility support, is expected to have a key role for future All-IP wireless networks. As the demands for delay sensitive multimedia applications ...
Comments