Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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.
['Muhammad Awais', 'Dominik Henrich']
I am writing a research paper on a idea Human-Robot Interaction in an Unknown Human Intention Scenario
[{"from": "human", "value": "I am writing a research paper on a idea Human-Robot Interaction in an Unknown Human Intention Scenario"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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."}]