DMP-Based Reactive Robot-to-Human Handover in Perturbed Scenarios

While seemingly simple, handover requires joint coordinate efforts from both partners, commonly in dynamic collaborative scenarios. Practically, humans are able to adapt and react to their partner’s movement to ensure seamless interaction against perturbations or interruptions. However, literature on robotic handover usually considers straightforward scenarios. We propose an online trajectory generation method based on Dynamic Movement Primitives to enable reactive robot behavior in perturbed scenarios. Thus, the robot is able to adapt to human motion (stopping should the handover be interrupted while persisting through minor disturbances on the partner’s trajectory). Qualitative analysis is conducted to demonstrate the capability of the proposed controller with different parameter settings and against a non-reactive implementation. This analysis shows that controllers with reactive parameter settings produce robot trajectories that can be deemed as more coordinated under perturbation. Additionally, a randomized trial with participants is conducted to validate the approach by assessing the subject perception through a questionnaire while measuring task completion and robot idle time. Our method has been shown to significantly increase the subjective perception of the interaction with no statistically significant deterioration in task performance metrics under one of the two sets of parameters analyzed. This paper represents a first step towards the introduction of reactive controllers in handover tasks that explicitly consider perturbations and interruptions