The Grasp Strategy of a Robot Passer Influences Performance and Quality of the Robot-Human Object Handover

Abstract

Task-aware robotic grasping is critical if robots are to successfully cooperate with humans. The choice of a grasp is multi-faceted; however, the task to perform primes this choice in terms of hand shaping and placement on the object. This grasping strategy is particularly important for a robot companion, as it can potentially hinder the success of the collaboration with humans. In this work, we investigate how different grasping strategies of a robot passer influence the performance and the perceptions of the interaction of a human receiver. Our findings suggest that a grasping strategy that accounts for the subsequent task of the receiver improves substantially the performance of the human receiver in executing the subsequent task. The time to complete the task is reduced by eliminating the need of a post-handover re-adjustment of the object. Furthermore, the human perceptions of the interaction improve when a task-oriented grasping strategy is adopted. The influence of the robotic grasp strategy increases as the constraints induced by the object's affordances become more restrictive. The results of this work can benefit the wider robotics community, with application ranging from industrial to household human-robot interaction for cooperative and collaborative object manipulation.

Human manipulation strategy when changing object deformability and task properties

“…Present results show that the GP for the rigid object was near the CoM [23] for the short-hole insertion, regardless of the level of accuracy required (Fig. 3a).…”

Affordance-Aware Handovers With Human Arm Mobility Constraints

“…In the latter case, they report a notable preference from the receivers. In a robot-to-human setting, there have been fewer explorations of handover methods that directly consider object affordances [6], [16], [17]. In [17] the authors perform object part segmentation and manually assign the corresponding affordances with the purpose of maximising the user's convenience.…”

PrendoSim: Proxy-Hand-Based Robot Grasp Generator

“…There are presently three built-in objects in Pren-doSim: a pair of scissors, a hammer, and a screwdriver. We selected these models from the YCB dataset (Calli et al, 2017), because they are familiar to most people and offer different degrees of constraint difficulty for their intended use (Ortenzi et al, 2020a). All objects are considered to be rigid, i.e., the blades of the scissors cannot open and close.…”