Resolving conflicts during human-robot co-manipulation

Al-Saadi, Zaid; Hamad, Yahya M; Aydin, Yusuf; Kucukyilmaz, Ayse; Basdogan, Cagatay

Abstract

This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts.

Citation

Al-Saadi, Z., Hamad, Y. M., Aydin, Y., Kucukyilmaz, A., & Basdogan, C. (2023). Resolving conflicts during human-robot co-manipulation. In HRI '23: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (243-251). https://doi.org/10.1145/3568162.3576969

• Conference Name: ACM/IEEE International Conference on Human-Robot Interaction
• Conference Location: Stockholm, Sweden
• End Date: Mar 15, 2023
• Acceptance Date: Dec 3, 2022
• Online Publication Date: Mar 13, 2023
• Publication Date: Mar 13, 2023
• Deposit Date: Jan 6, 2023
• Publicly Available Date: Mar 13, 2023
• Pages: 243-251
• Book Title: HRI '23: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction
• ISBN: 9781450399647
• DOI: https://doi.org/10.1145/3568162.3576969
• Keywords: Computer systems organization; Embedded and cyber- physical systems; Robotics
• Public URL: https://nottingham-repository.worktribe.com/output/15718311
• Publisher URL: https://dl.acm.org/doi/10.1145/3568162.3576969
• Related Public URLs: https://humanrobotinteraction.org/2023/