Robust Skill

Robust skill acquisition in robotics and AI focuses on enabling agents to learn and apply skills effectively across diverse and unpredictable environments, minimizing the need for extensive, task-specific training data. Current research emphasizes unsupervised and reinforcement learning methods, often incorporating techniques like contrastive learning, hierarchical reinforcement learning, and the use of large language models to guide skill selection and improve generalization. This research is crucial for advancing autonomous systems, particularly in areas like robotics and autonomous driving, where adaptability and robustness are paramount for safe and reliable operation.