LangGrasp: Leveraging Fine-Tuned LLMs for Language Interactive Robot Grasping with Ambiguous Instructions

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 leverage their robust commonsense understanding and environmental perception capabilities, thereby deducing implicit intents from linguistic instructions and clarifying task requirements along with target manipulation objects. Furthermore, our designed point cloud localization module, guided by 2D part segmentation, enables partial point cloud localization in scenes, thereby extending grasping operations from coarse-grained object-level to fine-grained part-level manipulation. Experimental results show that the LangGrasp framework accurately resolves implicit intents in ambiguous instructions, identifying critical operations and target information that are unstated yet essential for task completion. Additionally, it dynamically selects optimal grasping poses by integrating environmental information. This enables high-precision grasping from object-level to part-level manipulation, significantly enhancing the adaptability and task execution efficiency of robots in unstructured environments. More information and code are available here: https://github.com/wu467/LangGrasp.

LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees

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 to reason the descriptive steps of tasks. In order to enable ChatGPT to understand the environment, semantic maps are constructed by an object recognition algorithm. Then, we design a Parser module based on Bidirectional Encoder Representations from Transformers (BERT) to parse these steps into initial BTs. Subsequently, a BTs Update algorithm is proposed to expand the initial BTs dynamically to control robots to perform adaptive tasks. Different from other LLM-based methods for complex robotic tasks, our method outputs variable BTs that can add and execute new actions according to environmental changes, which is robust to external disturbances. Our method is validated with simulation in different practical scenarios.