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：人形机器人将因其多样化的动作能力而能够协助人类日常生活。然而，这些机器人在需要一定自主性以学习和探索的同时，也应尊重访问控制等多方面的约束。本文探索了将隐私、安全及访问控制约束与机器人任务规划方法相结合的新领域。我们报告了关于经典符号化方法、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路的初步结果。通过分析它们的权衡关系，我们得出结论：混合方法不可或缺，并由此为新兴的神经符号人工智能领域提出了一个新的应用案例。