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Unit information: Human-Robot Interaction (UWE, UFMFHP-15-M) in 2021/22

Please note: It is possible that the information shown for future academic years may change due to developments in the relevant academic field. Optional unit availability varies depending on both staffing, student choice and timetabling constraints.

Unit name Human-Robot Interaction (UWE, UFMFHP-15-M)
Unit code EMATM0043
Credit points 15
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Giuliani
Open unit status Not open
Pre-requisites

None

Co-requisites

None

School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

Description

This module will provide an overview of human-robot interaction (HRI) as a research field. It will cover different contexts in which humans interact with robots now and in the future and how these contexts shape the physical and social constraints of the interaction. For example, we will look at the assisted living context, in which robots support humans in their homes and thus have to display socially appropriate behaviours. In contrast to that, we will look at collaborative robots in industrial settings, in which knowledge about task planning and part assembly is more important. The module also introduces the technologies needed in a HRI system, for example vision processing, speech recognition and natural language understanding, reasoning, output generation, and cognitive robot architectures. We will introduce the human factors that are relevant for a successful HRI (e.g., acceptance, trust, cognitive load) and how to measure these factors. Finally, the module describes how to set up, execute, and analyse HRI user studies.

Intended learning outcomes

Refer to UWE unit level guidance.

Teaching details

Refer to UWE unit level guidance.

Assessment Details

Refer to UWE unit level guidance.

Reading and References

  • Bartneck, C., Kulić, D., Croft, E. and Zoghbi, S. (2009) Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. International Journal of Social Robotics. 1 (1), pp. 71–81. doi:10.1007/s12369-008-0001-3.
  • Breazeal, C. (2003) Toward sociable robots. Robotics and Autonomous Systems. 42 (3-4), pp. 167–175. doi:10.1016/S0921-8890(02)00373-1.
  • Goodrich, M.A. and Schultz, A.C. (2007) Human-Robot Interaction: A Survey. Foundations and Trends® in Human-Computer Interaction. 1 (3), pp. 203–275. doi:10.1561/1100000005.
  • Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., Errico, F. D’ and Schroeder, M. (2012) Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing. IEEE Transactions on Affective Computing. 3 (1), pp. 69–87. doi:10.1109/T-AFFC.2011.27.
  • Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I. and Grinter, R.E. (2011) Evaluating Human-Robot Interaction. International Journal of Social Robotics. 3 (1), pp. 53–67. doi:10.1007/s12369-010-0081-8.

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