  The field of Human-Robot Collaboration (HRC) has seen a considerable amount
of progress in recent years. Thanks in part to advances in control and
perception algorithms, robots have started to work in increasingly unstructured
environments, where they operate side by side with humans to achieve shared
tasks. However, little progress has been made toward the development of systems
that are truly effective in supporting the human, proactive in their
collaboration, and that can autonomously take care of part of the task. In this
work, we present a collaborative system capable of assisting a human worker
despite limited manipulation capabilities, incomplete model of the task, and
partial observability of the environment. Our framework leverages information
from a high-level, hierarchical model that is shared between the human and
robot and that enables transparent synchronization between the peers and mutual
understanding of each other's plan. More precisely, we firstly derive a
partially observable Markov model from the high-level task representation; we
then use an online Monte-Carlo solver to compute a short-horizon
robot-executable plan. The resulting policy is capable of interactive
replanning on-the-fly, dynamic error recovery, and identification of hidden
user preferences. We demonstrate that the system is capable of robustly
providing support to the human in a realistic furniture construction task.
