Abstract
Many robotics tasks require a robot to share the same workspace with humans. In such settings, it is important that the robot performs in such a way that does not cause distress to humans in the workspace. In this paper, we address the problem of designing robot controllers which minimize the stress caused by the robot while performing a given task. We present a novel, data-driven algorithm which computes human-friendly trajectories. The algorithm utilizes biofeedback measurements and combines a set of geometric controllers to achieve human friendliness. We evaluate the comfort level of the human using a Galvanic Skin Response (GSR) sensor. We present results from a human tracking task, in which the robot is required to stay within a specified distance without causing high stress values.
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References
Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. In ICML ’04: proceedings of the twenty-first international conference on machine learning (p. 1). New York: ACM.
Abowd, G. D., Dey, A. K., Orr, R., & Brotherton, J. A. (1997). Context-awareness in wearable and ubiquitous computing. In ISWC (pp. 179–180).
Bersak, D., McDarby, G., Augenblick, N., McDarby, P., McDonnell, D., McDonald, B., & Karkun, R. (2001). Intelligent biofeedback using an immersive competitive environment. In Ubicomp.
Brooks, A. G., & Arkin, R. C. (2007). Behavioral overlays for non-verbal communication expression on a humanoid robot. Autonomous Robots, 22(1), 55–74.
Christensen, H. I., & Pacchierotti, E. (2005). Embodied social interaction for robots. In K. Dautenhahn (Ed.), AISB-05 (pp. 40–45), Hertsfordshire, April 2005.
Clynes, D. M. (1997). Sentics: the touch of emotions. Surbiton: Anchor.
Fong, T. W., Nourbakhsh, I., & Dautenhahn, K. (2002). A survey of socially interactive robots: concepts, design, and applications (Tech. Report CMU-RI-TR-02-29). Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, December 2002.
Hinds, P. J., Roberts, T. L., & Jones, H. (2004). Whose job is it anyway? A study of human–robot interaction in a collaborative task. Human–Computer Interaction, 19, 151–181.
Kulyukin, V. A., & Gharpure, C. (2006). Ergonomics-for-one in a robotic shopping cart for the blind. In HRI ’06: proceeding of the 1st ACM SIGCHI/SIGART conference on human–robot interaction (pp. 142–149). New York: ACM.
Kulyukin, V., Gharpure, C., Nicholson, J., & Osborne, G. (2006). Robot-assisted wayfinding for the visually impaired in structured indoor environments. Autonomous Robots, 21(1), 29–41.
Lesh, N., Rich, C., & Sidner, C. L. (1999). Using plan recognition in human–computer collaboration. In UM ’99: proceedings of the seventh international conference on user modeling, Secaucus, NJ, USA (pp. 23–32). New York: Springer.
Mitsunaga, N., Smith, C., Kanda, T., Ishiguro, H., & Hagita, N. (2005). Robot behavior adaptation for human–robot interaction based on policy gradient reinforcement learning. In IEEE/RSJ international conference on intelligent robots and systems (IROS2005) (pp. 1594–1601).
Pacchierotti, E., Christensen, H. I., & Jensfelt, P. (2005). Human-robot embodied interaction in hallway settings: a pilot user study. In IEEE ROMAN-05 (pp. 164–171), Hertsfordshire, August 2005.
Pentland, A. S. (2004). Healthwear: medical technology becomes wearable. Computer, 37(5), 42–49.
Picard, R. (1995) Affective computing (Tech. Report 321). MIT Media Laboratory, Perceptual Computing Section, November 1995.
Plutchik, R. (1980). A general psychoevolutionary theory of emotion. In R. Plutchik, H. Kellerman (Eds.), Emotion research, theory, and experience, Vol. 1: theories of emotion (pp. 3–33). New York: Academic Press.
Ramachandran, D., & Amir, E. (2007) Bayesian inverse reinforcement learning. In 20th international joint conference on artificial intelligence (IJCAI’07).
Rani, P., & Sarkar, N. (2005). Making robots emotion-sensitive—preliminary experiments and results. In IEEE international workshop on robot and human interactive communication (ROMAN 2005) (pp. 13–15).
Rani, P., Sarkar, N., Smith, C. A., & Kirby, L. D. (2004). Anxiety detecting robotic systems—towards implicit human–robot collaboration. Robotica, 22(1), 85–95.
Rani, P., Liu, C., & Sarkar, N. (2006). Affective feedback in closed loop human–robot interaction. In HRI ’06: proceeding of the 1st ACM SIGCHI/SIGART conference on human–robot interaction (pp. 335–336). New York: ACM.
Roy, N., Pineau, J., & Thrun, S. (2000). Spoken dialogue management using probabilistic reasoning. In ACL ’00: proceedings of the 38th annual meeting on association for computational linguistics (pp. 93–100). Morristown: Association for Computational Linguistics.
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Meisner, E., Isler, V. & Trinkle, J. Controller design for human-robot interaction. Auton Robot 24, 123–134 (2008). https://doi.org/10.1007/s10514-007-9054-7
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DOI: https://doi.org/10.1007/s10514-007-9054-7