Reasoning about object handover configurations allows an assistive agent to estimate the appropriateness of handover for a receiver with different arm mobility capacities.  While there are existing approaches for estimating the effectiveness of handovers, their findings are limited to users without arm mobility impairments and to specific objects. Therefore, current state-of-the-art approaches are unable to hand over novel objects to receivers with different arm mobility capacities. We propose a method that generalises handover behaviours to previously unseen objects, subject to the constraint of a user’s arm mobility levels and the task context. We propose a heuristic-guided hierarchically optimised cost whose optimisation adapts object configurations for receivers with low arm mobility. This also ensures that the robot grasps consider the context of the user’s upcoming task, i.e., the usage of the object. To understand preferences over handover configurations, we report on the findings of an online study, wherein we presented different handover methods, including ours, to 259 users with different levels of arm mobility. We find that people’s preferences over
handover methods are correlated to their arm mobility capacities. We encapsulate these preferences in a statistical relational learner
(SRL) that is able to reason about the most suitable handover configuration given a receiver’s arm mobility and upcoming task.
Using our SRL model, we obtained an average handover accuracy of 90:8% when generalising handovers to novel objects.