When to assist? - Modelling human behaviour for hybrid assembly systems

Joint 41st International Symposium on Robotics and 6th German Conference on Robotics 2010, ISR/ROBOTIK 2010

Volumn 1, Issue , 2010, Pages 165-170

  Huber, Markus ^a   Knoll, Alois ^b   Brandt, Thomas ^a   Glasauer, Stefan ^a  

^a UNIVERSITY OF MUNICH   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL SYSTEMS; ASSISTIVE ROBOTS; HUMAN BEHAVIOURS; HUMAN ROBOT INTERACTION (HRI); HYBRID ASSEMBLY; LINEAR DEPENDENCY; MUTUAL UNDERSTANDING; PHYSICAL INTERACTIONS;

ROBOTICS;

EID: 78649879095     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Foster M.E., Giuliani M., Müller T., Rickert M., Knoll A., Erlhagen W., Bicho E., Hipolito N., Louro L.: Combining Goal Inference and Natural-Language Dialogue for Human-Robot Joint Action, in Proceedings of the CIMA Workshop at ECAI, 2008
2. Hinds P.J., Roberts T.L., Jones H.: Whose job is it anyway? A study of human-robot interaction in a collaborative task, in Human-Computer Interaction 19, p. 151-181, 2004
3. Lenz C., Nair S., Rickert M., Knoll A., Rösel W., Gast J., Wallhoff F.: Joint-action for humans and industrial robots for assembly tasks, In Human Interactive Communication, RO-MAN, Munich, Germany, p. 130 -135, 2008
4. Fong T., Nourbakhsh I., Dautenhahn K.: A survey of socially interactive robots, Robotics and Autonomous Systems, 42, p. 143-166, 2003
5. Oztop, E. and Wolpert, D. and Kawato, M.: Mental state inference using visual control parameters, Cognitive Brain Research, 22, p.129-151, 2005
6. Cuijpers, R. H. and van Schie, H. T. and Koppen, M. and Erlhagen, W. and Bekkering, H.: Goals and means in action observation: A computational approach, Neural Networks, 19, p. 311-322, 2006
7. Erlhagen W., Mukovskiy A., Bicho E., Panin G., Kiss C., Knoll A., van Schie H., Bekkering H.: Action-understanding and Imitation Learning in a Robot-Human Task, Artificial Neural Networks: Biological Inspirations, p. 261-268, Lecture Notes on Computer Science, Springer Verlag, 2005.
8. Maynard H.B., Stegemerten G.J., Schwab J.L.: Methods-time measurement, McGraw-Hill New York, 1948
9. Miyakawa S., Ohashi, T.: The Hitachi Assembly Evaluation Method (AEM), Proc. International Conference on Product Design for Assembly, Newport, Rhode Island, 1986
10. Boothroyd G., Alting L.: Design for assembly and disassembly, CIRP annals, 41, p.624-636, 1992
11. Fish L.A., Drury C.G., Helander M.G.: Operatorspecific model: An assembly time prediction model, Human Factors and Ergonomics in Manufacturing, 7, p.211-235, 1997
12. Richardson M., Jones G., Torrance M., Baguley T.: Identifying the task variables that predict object assembly difficulty, Human factors, 48, p.511-525, 2006
13. Zaeh M.F., Wiesbeck M., Stork S., Schubö A.: A multi-dimensional measure for determining the complexity of manual assembly operations, Production Engineering, p.1-8, 2009
14. Huber M., Knoll A., Brand T., Glasauer S.: Handingover a cube: spatial features of physical joint action. Annals of the New York Academy of Sciences 1431, p. 380-382, 2008
15. Kalman R.E.: A new approach to linear filtering and prediction problems, Journal of basic Engineering, 82, p.35-45,1960
16. Huber M., Radrich H., Wendt C., Glasauer S., Knoll A., Brandt T.: Evaluation of a novel biologically inspired Trajectory Generator in Human-Robot Interaction, in Human Interactive Communication, ROMAN Toyama, Japan, p. 639 - 644, 2009