Anca Dragan
ABSTRACT
We study the effect of familiarization on the predictability of robot motion. Predictable motion is motion that matches the observer's expectation. Because of the difficulty robots have in learning motion from user demonstrations, we explore the idea of having users learn from robot demonstrations --- how accurate do users get at predicting how the robot will move? We find that although familiarization significantly increases predictability, its utility depends on how natural the motion is. Overall, familiarization shows great promise, but needs to be combined with other methods that generate appropriate motion with which to be familiarized.
- B. Akgun, M. Cakmak, J. W. Yoo, and A. L. Thomaz. Trajectories and keyframes for kinesthetic teaching: a human-robot interaction perspective. In HRI, pages 391--398, 2012. Google Scholar
Digital Library
- 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 Workshop on HRI, 2006.Google Scholar
- A. Arita, K. Hiraki, T. Kanda, and H. Ishiguro. Can we talk to robots? ten-month-old infants expected interactive humanoid robots to be talked to by persons. Cognition, 95(3):B49--B57, 2005.Google ScholarCross Ref
- M. Beetz, F. Stulp, P. Esden-Tempski, A. Fedrizzi, U. Klank, I. Kresse, A. Maldonado, and F. Ruiz. Generality and legibility in mobile manipulation. Autonomous Robots, 28:21--44, 2010. Google ScholarDigital Library
- D. Bertram, J. Kuffner, R. Dillmann, and T. Asfour. An integrated approach to inverse kinematics and path planning for redundant manipulators. In ICRA, 2006.Google ScholarCross Ref
- S. Calinon, F. Guenter, and A. Billard. On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans. on Systems, Man, and Cybernetics, 37(2):286--298, 2007. Google ScholarDigital Library
- R. B. D'AGOSTINO. A second look at analysis of variance on dichotomous data. Journal of Educational Measurement, 8(4):327--333, 1971.Google ScholarCross Ref
- M. Desai, M. Medvedev, M. Vázquez, S. McSheehy, S. Gadea-Omelchenko, C. Bruggeman, A. Steinfeld, and H. Yanco. Effects of changing reliability on trust of robot systems. In Human-Robot Interaction (HRI), 2012 7th ACM/IEEE International Conference on, pages 73--80. IEEE, 2012. Google ScholarDigital Library
- A. Dragan, K. Lee, and 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
- T. Huang, Z. Li, M. Li, D. G. Chetwynd, and C. M. Gosselin. Conceptual design and dimensional synthesis of a novel 2-dof translational parallel robot for pick-and-place operations. Journal of Mechanical Design, 126:449, 2004.Google ScholarCross Ref
- K. Kamewari, M. Kato, T. Kanda, H. Ishiguro, and K. Hiraki. Six-and-a-half-month-old children positively attribute goals to human action and to humanoid-robot motion. Cognitive Development, 20(2):303--320, 2005.Google ScholarCross Ref
- C. D. Kidd and C. Breazeal. Human-robot interaction experiments: Lessons learned. In Proceeding of AISB, volume 5, pages 141--142, 2005.Google Scholar
- T. Komatsu and S. Yamada. Adaptation gap hypothesis: How differences between users' expected and perceived agent functions affect their subjective impression. Journal of Systemics, Cybernetics and Informatics, 9(1):67--74, 2011.Google Scholar
- F. Lacquaniti and J. Soechting. Coordination of arm and wrist motion during a reaching task. J Neurosci., 2:399--408, April 1982.Google ScholarCross Ref
- D. P. Miller. Assistive robotics: an overview. In Assistive Technology and Artificial Intelligence, pages 126--136. 1998. Google ScholarDigital Library
- R. Mitchell and F. Myles. Second language learning theories. 2004.Google Scholar
- B. Mutlu and J. Forlizzi. Robots in organizations: the role of workflow, social, and environmental factors in human-robot interaction. In HRI, 2008. Google ScholarDigital Library
- N. Ratliff, J. A. Bagnell, and M. Zinkevich. Maximum margin planning. In ICML, 2006. Google ScholarDigital Library
- S. Schaal. Dynamic movement primitives-a framework for motor control in humans and humanoid robotics. In Adaptive Motion of Animals and Machines, pages 261--280. 2006.Google ScholarCross Ref
- R. M. Siino and P. J. Hinds. Robots, gender & sensemaking: Sex segregation's impact on workers making sense of a mobile autonomous robot. In ICRA, 2005.Google ScholarCross Ref
- S. Srinivasa, D. Berenson, M. Cakmak, A. Collet, M. Dogar, A. Dragan, R. Knepper, T. Niemueller, K. Strabala, M. V. Weghe, and J. Ziegler. Herb 2.0: Lessons learned from developing a mobile manipulator for the home. Proc. of the IEEE, 2012.Google ScholarCross Ref
- K. Stubbs, D. Wettergreen, and I. Nourbakhsh. Using a robot proxy to create common ground in exploration tasks. In HRI, 2008. Google ScholarDigital Library
- K. Sundin, L. Jansson, and A. Norberg. Communicating with people with stroke and aphasia: understanding through sensation without words. Journal of clinical nursing, 9(4):481--488, 2000.Google Scholar
- A. L. Thomaz and M. Cakmak. Learning about objects with human teachers. In HRI, 2009. Google ScholarDigital Library
- S. Upson. tongue vision. Spectrum, IEEE, 44(1):44--45, 2007. Google ScholarDigital Library
- K. E. Weick. Sensemaking in organizations, volume 3. 1995.Google Scholar
- B. D. Ziebart, A. Maas, J. A. Bagnell, and A. Dey. Maximum entropy inverse reinforcement learning. In AAAI, 2008. Google ScholarDigital Library
- M. Zucker, N. Ratliff, A. Dragan, M. Pivtoraiko, M. Klingensmith, C. Dellin, J. A. D. Bagnell, and S. Srinivasa. Chomp: Covariant hamiltonian optimization for motion planning. IJRR, 2013. Google ScholarDigital Library
Index Terms
- Familiarization to robot motion
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