LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks
This work addresses human-robot collaboration for manipulation tasks, but it appears incremental as it combines existing methods like LLM, YOLO, and DMP without introducing a fundamentally new paradigm.
The paper tackles the problem of autonomous robotic manipulation by using a Large Language Model (LLM) to convert high-level language commands into executable motion sequences, integrating YOLO-based perception and action correction via teleoperation and Dynamic Movement Primitives (DMP) to enhance practicality and generalizability.
This paper presents a novel approach to enhance autonomous robotic manipulation using the Large Language Model (LLM) for logical inference, converting high-level language commands into sequences of executable motion functions. The proposed system combines the advantage of LLM with YOLO-based environmental perception to enable robots to autonomously make reasonable decisions and task planning based on the given commands. Additionally, to address the potential inaccuracies or illogical actions arising from LLM, a combination of teleoperation and Dynamic Movement Primitives (DMP) is employed for action correction. This integration aims to improve the practicality and generalizability of the LLM-based human-robot collaboration system.