Unseen Environment

Research on "unseen environments" focuses on enabling robots and AI systems to operate effectively in situations not encountered during training. Current efforts concentrate on developing robust perception and navigation methods using techniques like diffusion models, topological data analysis, and large language models integrated with visual-language models, often incorporating continual learning and self-supervised learning strategies to improve generalization. This research is crucial for advancing autonomous systems in diverse real-world applications, such as robotics, augmented reality, and autonomous driving, by improving their adaptability and reliability in unpredictable settings.