GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments

In recent years, the gripping use of unmanned aerial vehicles (UAVs) has emerged as a new trending research direction [1, 2]. However, the grabbing scenes in the open world are very complex, which leads to the development of robotic grasping systems with advanced cognitive and adaptable grasping capabilities. To achieve high-level cognitive abilities, reinforcement learning embodiment is studied[3, 4]. In [3], Scalable Deep Reinforcement Learning is used to handle large amounts of off-policy image data for complex tasks like grasping. However, RL-based embodiment has posed challenges in terms of generalization capability, sample-effectiveness capability, and profound reasoning capability, especially in dynamic and uncertain real environments. Recently, Large multimodal models (LMMs), such as MiniGPT-4 [5] and LLaVA [6], have exhibited impressive performance in the domains of natural instruction-following and visual cognition. Therefore, LMMs are integrated with the physical world in the embodied agent. Apart from RL algorithms for specific tasks, LMMs-based agents have generalization capabilities [7, 8] though fine-tune methods, such as human demonstrations [9], vision-language cross-modal connector[10], ever-growing skill library [11] and so on. On-policy (RL) algorithms face challenges in terms of sample efficiency.