Browsing by Author "Al-Saadi, Zaid"

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Citation - WoS: 3
Citation - Scopus: 4
A Machine Learning Approach To Resolving Conflicts in Physical Human-Robot Interaction
(Association for Computing Machinery, 2025) Ulas Dincer, Enes; Al-Saadi, Zaid; Hamad, Y.M.; Aydın, Yusuf; Kucukyilmaz, A.; Basdogan, C.
As artificial intelligence techniques become more sophisticated, we anticipate that robots collaborating with humans will develop their own intentions, leading to potential conflicts in interaction. This development calls for advanced conflict resolution strategies in physical human-robot interaction (pHRI), a key focus of our research. We use a machine learning (ML) classifier to detect conflicts during co-manipulation tasks to adapt the robot's behavior accordingly using an admittance controller. In our approach, we focus on two groups of interactions, namely "harmonious"and "conflicting,"corresponding respectively to the cases of the human and the robot working in harmony to transport an object when they aim for the same target, and human and robot are in conflict when human changes the manipulation plan, e.g. due to a change in the direction of movement or parking location of the object.Co-manipulation scenarios were designed to investigate the efficacy of the proposed ML approach, involving 20 participants. Task performance achieved by the ML approach was compared against three alternative approaches: (a) a rule-based (RB) Approach, where interaction behaviors were rule-derived from statistical distributions of haptic features; (b) an unyielding robot that is proactive during harmonious interactions but does not resolve conflicts otherwise, and (c) a passive robot which always follows the human partner. This mode of cooperation is known as "hand guidance"in pHRI literature and is frequently used in industrial settings for so-called "teaching"a trajectory to a collaborative robot.The results show that the proposed ML approach is superior to the others in task performance. However, a detailed questionnaire administered after the experiments, which contains several metrics, covering a spectrum of dimensions to measure the subjective opinion of the participants, reveals that the most preferred mode of interaction with the robot is surprisingly passive. This preference indicates a strong inclination toward an interaction mode that gives more control to humans and offers less demanding interaction, even if it is not the most efficient in task performance. Hence, there is a clear trade-off between task performance and the preferred mode of interaction of humans with a robot, and a well-balanced approach is necessary for designing effective pHRI systems in the future. © 2025 Copyright held by the owner/author(s).
Citation - WoS: 11
Citation - Scopus: 11
Resolving Conflicts During Human-Robot Co-Manipulation
(IEEE Computer Society, 2023) Al-Saadi, Zaid; Hamad, Yahya M.; Aydin, Yusuf; Kucukyilmaz, Ayse; Basdogan, Cagatay
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.