  We present a simple approach to in-hand cube reconfiguration. By simplifying
planning, control, and perception as much as possible, while maintaining robust
and general performance, we gain insights into the inherent complexity of
in-hand cube reconfiguration. We also demonstrate the effectiveness of
combining GOFAI-based planning with the exploitation of environmental
constraints and inherently compliant end-effectors in the context of dexterous
manipulation. The proposed system outperforms a substantially more complex
system for cube reconfiguration based on deep learning and accurate physical
simulation, contributing arguments to the discussion about what the most
promising approach to general manipulation might be. Project website:
https://rbo.gitlab-pages.tu-berlin.de/robotics/simpleIHM/
