A Machine Learning Approach to Resolving Conflicts in Physical Human-Robot Interaction

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 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 such as a change in the direction of movement or parking location of the object, respectively. 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 towards 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.