HELP Request

Research on "HELP" requests focuses on developing systems and algorithms that enable agents (robots, AI models, etc.) to proactively seek assistance when needed, improving efficiency and robustness. Current work explores various approaches, including hierarchical embeddings for log parsing, attention mechanisms for modeling driver risk perception, and affordance-based uncertainty estimation for robotic planning, often leveraging large language models (LLMs) for improved performance and contextual understanding. This research is significant because it addresses limitations in autonomous systems by enabling more effective human-agent collaboration and improving the reliability of AI in complex or uncertain environments, with applications ranging from healthcare to industrial automation.