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.

翻译：仿人机器人因其多功能的行动能力，有望辅助人类日常生活。然而，这些机器人既需要一定程度的自主性来学习和探索，也应遵守访问控制等各类约束条件。我们探索了将隐私、安全和访问控制约束与机器人任务规划方法相结合的新领域。本文报告了基于经典符号方法、深度学习的神经网络、以及利用大语言模型作为知识库的现代思想的初步研究成果。通过分析这些方法的权衡折中，我们得出结论：需要采取混合方法，并由此为新兴的神经符号人工智能领域提出了新的应用场景。