  Humanoid robots will be able to assist humans in their daily life, in
particular due to their versatile action capabilities. However, while these
robots need a certain degree of autonomy to learn and explore, they also should
respect various constraints, for access control and beyond. We explore the
novel field of incorporating privacy, security, and access control constraints
with robot task planning approaches. We report preliminary results on the
classical symbolic approach, deep-learned neural networks, and modern ideas
using large language models as knowledge base. From analyzing their trade-offs,
we conclude that a hybrid approach is necessary, and thereby present a new use
case for the emerging field of neuro-symbolic artificial intelligence.
