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：仿人机器人因其多样化的动作能力，有望在人类日常生活中提供辅助。然而，这些机器人在需要一定程度的自主性以进行学习和探索的同时，还应遵守访问控制等方面的各种约束。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这一新领域。我们报告了关于经典符号方法、深度学习的神经网络以及利用大型语言模型作为知识库的现代思想的初步研究成果。通过分析它们的权衡，我们得出结论：混合方法是必要的，从而为新兴的神经符号人工智能领域提出了一个新的应用场景。