  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 model (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.
