Embodied Intelligence Robots: Flexible Task Planning Framework and Multimodal Fusion Perception

Abstract

Rapid manufacturing evolution accelerates product iteration, requiring collaborative robots to rapidly adapt and deploy new action programs. Although large language models (LLMs) can generate logically consistent instructions, they lack contextual understanding of human–robot collaborative environments. We propose an understanding and perceiving the environment with requests model that fuses multimodal information to enable embodied robots to interpret user intent within environmental context. In addition, a flexible task planning framework for multiagent systems is introduced, allowing agents to form flexible teams according to task requirements. This framework grants agents reflective capabilities, enabling them to analyze failures and replan accordingly, thus enhancing task planning flexibility. Appropriately designed prompts for embodied intelligence robots are used to mitigate the hallucination effect of LLMs, further improving the accuracy of task planning when using LLMs. In collaborative aerospace component assembly experiments, the proposed system significantly improved natural-language-driven task execution, achieving a 10.80% improvement in final success rate.