The Community for Technology Leaders
Frontiers of Information Technology (2013)
Islamabad, Pakistan Pakistan
Dec. 16, 2013 to Dec. 18, 2013
pp: 89-94
In this paper an approach is introduced to human robot interaction in a known scenario with unknown human intentions. Initially, the robot reacts by copying the human action. As the human-robot interaction proceeds, the level of human-robot interaction improves. Before each reaction, the robot hypothesizes its potential actions and selects one that is found most suitable. The robot may also use the human-robot interaction history. Along with the history, the robot also considers the action randomness and heuristic based action predictions. As solution, a general reinforcement Learning (RL) based algorithm is proposed that suggests learning of human robot interaction in an unknown human intention scenario. A Particle Filter (PF) based algorithm is proposed to support the probabilistic action selection for human-robot interaction. The experiments for human-robot interaction are performed by a robotic arm involving the arrangement of known objects with unknown human intention. The task of the robot is to interact with the human according to the estimated human intention.
Human-robot interaction, History, Probabilistic logic, Trajectory, Robot kinematics, Service robots

M. Awais and D. Henrich, "Human-Robot Interaction in an Unknown Human Intention Scenario," 2013 11th International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2014, pp. 89-94.
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