Interactive Instruction

Interactive instruction focuses on enabling artificial agents, particularly robots and large language models (LLMs), to understand and execute complex tasks based on human-provided instructions, rather than relying solely on pre-programmed behaviors or large datasets of labeled examples. Current research emphasizes developing methods that allow agents to learn continuously from new instructions and adapt to unseen environments, often employing techniques like continual learning, multimodal integration (combining text and visual information), and hierarchical reasoning to break down complex tasks into manageable sub-goals. This field is significant for advancing human-robot collaboration and creating more adaptable and user-friendly AI systems capable of performing a wider range of tasks in dynamic real-world settings.