Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Robust Action Selection by the Robot in Human-Robot Interaction (HRI) Environment

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Kamran Ashraf, Muhammad Asif

Kamran Ashraf and Muhammad Asif. Robust Action Selection by the Robot in Human-Robot Interaction (HRI) Environment. International Journal of Computer Applications 155(7):40-43, December 2016. BibTeX

	author = {Kamran Ashraf and Muhammad Asif},
	title = {Robust Action Selection by the Robot in Human-Robot Interaction (HRI) Environment},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {155},
	number = {7},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {40-43},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016912359},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


With the evolution in the auspicious field of Human Robot Interaction it is crucial to work for the Robust Action Selection on a robot’s end especially when the human exhibits an unknown behaviour. A particular human action may lead to more than one human behaviour(s) and when it comes to a robot as an assistant or a co-worker it is of vital concern to have some efficient method to select a suitable action performed by the robot in response. After exploring multiple techniques a novel method is suggested using RL based approach to cater the need of robust action selection with addition of domain knowledge. Experimentation is performed using hardware equipment including 4DOF Robotic Arm equipped with the Arduino Kit and 480x640 Camera. Very promising results have been found and future direction is discovered.


  1. R. Wilcox, S. Nikolaidis, and J. Shah, "Optimization of temporal dynamics for adaptive human-robot interaction in assembly manufacturing," in Robotics: Science and Systems, 2012.
  2. H. Goto, J. Miura, and J. Sugiyama, "Human-Robot Collaborative Assembly by On-line Human Action Recognition Based on an FSM Task Model," in Human-Robot Interaction 2013 Workshop on Collaborative Manipulation, 2013.
  3. Goodrich M. A and A. C Schultz, "Human-robot interaction: a survey," Foundations and trends in human-computer interaction, vol. 1, no. 3, pp. 203-275, 2007.
  4. R. O. Ambrose et al., "Robonaut: NASA’s space humanoid," IEEE Intelligent Systems and Their Applications, vol. 15, no. 4, pp. 57-63, 2000.
  5. M. Kaiser and R. Dillmann, "Building elementary robot skills from human," In Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2700-2705, 1996.
  6. Rosenstein. M and Barto. A., "Supervised actor-critic reinforcement learning," Handbook of learning and approximate dynamic programming, p. 359, 2004.
  7. Schmid. A, Weede. O, and Worn. H, "Proactive Robot Task Selection Given a Human Intention Estimate," Robot and Human interactive Communication, RO-MAN. 16th IEEE International Symposium, pp. 726-731.
  8. Friedman, Nir, Dan Geiger, and Moises Goldszmidt, "Bayesian network classifiers," Machine learning, vol. 29, no. 2-3, pp. 131-163, 1997.
  9. Hofmann, A. G., and B. C. Williams, "Intent Recognition for Human-Robot Interaction," Interaction Challenges for Intelligent Assistants, 2007.
  10. Del Moral and Pierre., "Feynman-Kac formulae," Genealogical and interacting particle approximations, 2004.
  11. Nummiaro, Katja, Esther Koller-Meier, and Luc Van Gool, "An adaptive color-based particle filter," An adaptive color-based particle filter, vol. 21, no. 1, pp. 99-110, 2003.
  12. Thrun and Sebastian., "Particle filters in robotics," In Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence, pp. 511-518, 2002.
  13. Awais M. and Henrich D., "Human-Robot Interaction in an Unknown Human Intention Scenario," In Frontiers of Information Technology (FIT), 2013 11th International Conference on. IEEE, vol. 89-94, 2013


Robotics, Human Robot Interaction, Service Robots, Reinforcement Learning