Embodied Instruction

Embodied instruction following (EIF) focuses on enabling robots to understand and execute complex instructions given in natural language within real-world environments. Current research emphasizes improving the robustness and generalizability of agents by incorporating historical data, handling ambiguous instructions, and leveraging large language models (LLMs) within hierarchical planning frameworks, often incorporating multimodal information fusion and contrastive learning techniques. This field is crucial for advancing autonomous robotics and human-robot interaction, with potential applications ranging from household assistance to more complex tasks in unstructured settings.