3 accepted papers
Large Language Models (LLMs) are now transforming the way robots learn to work in unpredictable environments, such as homes or small enterprises. A growing number of approaches are combining LLMs with Behavior Trees (BTs). Not only do user commands need to be interpreted into BTs that contain the ta
The existing language-driven grasping methods struggle to fully handle ambiguous instructions containing implicit intents. To tackle this challenge, we propose LangGrasp, a novel language-interactive robotic grasping framework. The framework integrates fine-tuned large language models (LLMs) to leve
Large Language Models (LLMs) have been widely utilized to perform complex robotic tasks. However, handling external disturbances during tasks is still an open challenge. This paper proposes a novel method to achieve robotic adaptive tasks based on LLMs and Behavior Trees (BTs). It utilizes ChatGPT t…