Social Cobots Anticipatory Decision-Making for Collaborative Robots Incorporating Unexpected Human Behaviors

• Published in: 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 398 - 406
• Main Authors: Görür, O. Can, Rosman, Benjamin, Sivrikaya, Fikret, Albayrak, Sahin
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 26.02.2018
• Series: ACM Conferences
• Subjects: Anticipatory Decision-Making; Collaboration; Computer systems organization > Embedded and cyber-physical systems > Robotics > Robotic autonomy; Computing methodologies > Artificial intelligence > Computer vision > Computer vision tasks > Activity recognition and understanding; Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Cognitive robotics; Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Reasoning about belief and knowledge; Computing methodologies > Artificial intelligence > Philosophical/theoretical foundations of artificial intelligence > Theory of mind; Data models; Decision making; Human-Robot Collaboration; Human-robot interaction; Intent Inference; Markov processes; Reflection; Training; human-robot collaboration; anticipatory decision-making; intent inference
• ISBN: 9781450349536; 1450349536
• ISSN: 2167-2148
• DOI: 10.1145/3171221.3171256

We propose an architecture as a robot»s decision-making mechanism to anticipate a human»s state of mind, and so plan accordingly during a human-robot collaboration task. At the core of the architecture lies a novel stochastic decision-making mechanism that implements a partially observable Markov decision process anticipating a human»s state of mind in two-stages. In the first stage it anticipates the human»s task related availability, intent (motivation), and capability during the collaboration. In the second, it further reasons about these states to anticipate the human»s true need for help. Our contribution lies in the ability of our model to handle these unexpected conditions: 1) when the human»s intention is estimated to be irrelevant to the assigned task and may be unknown to the robot, e.g., motivation is lost, another assignment is received, onset of tiredness, and 2) when the human»s intention is relevant but the human doesn»t want the robot»s assistance in the given context, e.g., because of the human»s changing emotional states or the human»s task-relevant distrust for the robot. Our results show that integrating this model into a robot»s decision-making process increases the efficiency and naturalness of the collaboration.