Combining Ontological Knowledge and Large Language Model for User-Friendly Service Robots

Abstract

Lifestyle support through robotics is an increasingly promising field, with expectations for robots to take over or assist with chores like floor cleaning, table setting and clearing, and fetching items. The growth of AI, particularly foundation models, such as large language models (LLMs) and visual language models (VLMs), is significantly shaping this sector. LLMs, by facilitating natural interactions and providing vast general knowledge, are proving invaluable for robotic tasks. This paper focuses on the benefits of LLMs for "bring-me" tasks, where robots fetch specific items for users, often based on ambiguous instructions. Our previous efforts utilized an ontology extended to handle environmental data to resolve such ambiguities, but faced limitations when unresolvable ambiguities required user intervention for clarity. Here, we enhance our approach by integrating LLMs for providing additional commonsense knowledge, pairing it with ontological data to mitigate the issue of hallucinations and reduce the need for user queries, thus improving system usability. We present a system that merges these knowledge bases and assess its efficacy on "bring-me" tasks, aiming to provide a more seamless and efficient robotic assistance experience.