This research introduces a novel instruction-reasoning approach that enables robots to better understand how to interact with objects in 3D environments without predefined labels.

• Reformulates affordance detection as a language understanding problem rather than traditional semantic segmentation
• Leverages large language models to comprehend complex natural language instructions
• Demonstrates superior performance in open-vocabulary scenarios compared to conventional methods
• Enables robots to identify multiple possible uses for the same object in different contexts

For engineering teams, this advancement represents a significant step toward more flexible and adaptable robotic systems that can understand and interact with their environment in more human-like ways.

3D-AffordanceLLM: Harnessing Large Language Models for Open-Vocabulary Affordance Detection in 3D Worlds