Multimodal Agent

Multimodal agents are AI systems designed to perceive and interact with the world using multiple sensory modalities, such as vision and language, aiming to achieve complex tasks beyond the capabilities of unimodal agents. Current research focuses on developing robust agent architectures that integrate large language models (LLMs) and vision-language models (VLMs) with various memory and planning mechanisms, often employing reinforcement learning and imitation learning techniques. This field is significant because it pushes the boundaries of AI towards more general-purpose intelligence, with potential applications ranging from automating complex computer tasks and improving human-computer interaction to revolutionizing fields like healthcare and robotics.