Behavior Selection

Behavior selection in robotics focuses on enabling robots to autonomously choose appropriate actions from a repertoire of learned skills in response to dynamic and unpredictable environments. Current research emphasizes developing efficient algorithms, such as Quality-Diversity (QD) and its variants (e.g., Reset-Free QD, Dynamics-Aware QD), and leveraging the knowledge representation capabilities of vision-language models to improve behavior selection in complex scenarios. These advancements aim to enhance robot adaptability and robustness, reducing the need for extensive human supervision and paving the way for more autonomous and versatile robotic systems in real-world applications.