Language Planning

Language planning in robotics focuses on enabling robots to understand and execute complex tasks described in natural language. Current research emphasizes developing robust frameworks that integrate large language models (LLMs) with visual perception and symbolic planning, often employing hierarchical architectures and techniques like inverse planning and diffusion models to handle ambiguity, long-horizon tasks, and unseen objects. This work aims to improve robot adaptability and efficiency in real-world scenarios, bridging the gap between human-understandable instructions and robotic action, with significant implications for assistive robotics and automation.