How to Raise a Robot -- A Case for Neuro-Symbolic AI in Constrained Task Planning for Humanoid Assistive Robots
This addresses the need for constrained autonomy in humanoid robots to assist humans safely, but it is incremental as it builds on existing neuro-symbolic AI concepts for a specific application.
The paper tackles the problem of integrating privacy, security, and access control constraints into task planning for humanoid assistive robots, concluding that a hybrid neuro-symbolic AI approach is necessary based on analysis of trade-offs among symbolic, neural, and large language model methods.
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.