Proposition of Affordance-Driven Environment Recognition Framework Using Symbol Networks in Large Language Models
This work addresses the challenge of applying implicit common-sense knowledge for robots to coexist with humans, representing an incremental improvement in affordance-driven AI systems.
The study tackled the problem of enabling robots to understand dynamic situations by automatically acquiring affordances from large language models (LLMs), and demonstrated that reconstructing LLM outputs into symbol networks can extract context-dependent affordances with high explainability.
In the quest to enable robots to coexist with humans, understanding dynamic situations and selecting appropriate actions based on common sense and affordances are essential. Conventional AI systems face challenges in applying affordance, as it represents implicit knowledge derived from common sense. However, large language models (LLMs) offer new opportunities due to their ability to process extensive human knowledge. This study proposes a method for automatic affordance acquisition by leveraging LLM outputs. The process involves generating text using LLMs, reconstructing the output into a symbol network using morphological and dependency analysis, and calculating affordances based on network distances. Experiments using ``apple'' as an example demonstrated the method's ability to extract context-dependent affordances with high explainability. The results suggest that the proposed symbol network, reconstructed from LLM outputs, enables robots to interpret affordances effectively, bridging the gap between symbolized data and human-like situational understanding.