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.

Citation

Al-Saadi, Z., Hamad, Y. M., Aydin, Y., Kucukyilmaz, A., & Basdogan, C. (in press). Resolving Conflicts During Human-Robot Co-Manipulation. . https://doi.org/10.1145/3568162.3576969

• Conference Name: 18th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2023))
• Conference Location: Stockholm, Sweden
• Start Date: Mar 13, 2023
• End Date: Mar 16, 2023
• Acceptance Date: Dec 3, 2022
• Deposit Date: Jan 6, 2023
• Publisher: Association for Computing Machinery (ACM)
• 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
• Related Public URLs: https://humanrobotinteraction.org/2023/