  Humans perform everyday tasks using a combination of locomotion and
manipulation skills. Building a system that can handle both skills is essential
to creating virtual humans. We present a physically-simulated human capable of
solving box rearrangement tasks, which requires a combination of both skills.
We propose a hierarchical control architecture, where each level solves the
task at a different level of abstraction, and the result is a physics-based
simulated virtual human capable of rearranging boxes in a cluttered
environment. The control architecture integrates a planner, diffusion models,
and physics-based motion imitation of sparse motion clips using deep
reinforcement learning. Boxes can vary in size, weight, shape, and placement
height. Code and trained control policies are provided.
