Passer Kinematic Cues for Object Weight Prediction in a Simulated Robot-Human Handover

Object handovers, a seemingly straightforward action, involve a complex interplay of predictive and reactive control mechanisms in both partners. Understanding the cues that are used by humans to predict object properties is needed for planning natural robot handovers. In human-human interactions, the receiver can extract information from the passer's movement. Here, we show in a VR simulated agenthuman object handover, that the human receiver can use passer kinematic cues to predict the transported object's properties, such as weight, and preemptively adapt the grasping strategy towards them. We show that when the agent's movement is correlated to the object weight, humans can interpret this cue and produce proportional anticipatory grip forces before object release. This adaptation is learned even when objects are presented in a random order and is strengthened with the repeated presentation of the pairing. The outcome of this study contributes to a better understanding of non-verbal cues in handover tasks and enables more transparent and efficient real-world physical robot-human interactions.