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https://hdl.handle.net/20.500.11779/1996
Title: | Resolving conflicts during human-robot co-manipulation | Authors: | Al-Saadi, Zaid Hamad, Yahya M. Aydın, Yusuf Küçükyılmaz, Ayşe Başdoğan, Çağatay |
Keywords: | conflict resolution dyadic manipulation haptic features machine learning Physical human-robot interaction Classification (of information) Human robot interaction Man machine systems Robot programming Statistical tests Collaborative tasks Conflict Resolution Dyadic manipulation Haptic feature Haptics Human robots Machine learning approaches Machine-learning Physical humanrobot interaction (phri) Random forest classifier Machine learning |
Publisher: | IEEE Computer Society | Source: | Al-Saadi, Z., Hamad, Y. M., Aydin, Y., Kucukyilmaz, A., & Basdogan, C. (2023, March). Resolving Conflicts During Human-Robot Co-Manipulation. In Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (pp. 243-251). | 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. © 2023 Copyright is held by the owner/author(s). | Description: | UK Research and Innovation, UKRI: EP/S033718/2, EP/T022493/1, EP/V00784X This work is partially funded by UKRI and CHIST-ERA (HEAP: EP/S033718/2; Horizon: EP/T022493/1; TAS Hub: EP/V00784X). |
URI: | https://hdl.handle.net/20.500.11779/1996 https://doi.org/10.1145/3568162.3576969 |
ISBN: | 9781450399647 | ISSN: | 2167-2148 |
Appears in Collections: | Elektrik Elektronik Mühendisliği Bölümü koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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3568162.3576969.pdf | Full Text- Article | 3.74 MB | Adobe PDF | View/Open |
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