Action Primitive

Action primitives represent fundamental, reusable units of behavior in robotics and AI, aiming to decompose complex tasks into simpler, manageable components. Current research focuses on developing and learning these primitives using various methods, including deep reinforcement learning, imitation learning, and transformer-based architectures, often incorporating visual and tactile feedback for improved robustness and generalization. This work is significant for advancing robotic manipulation capabilities, enabling more adaptable and efficient robots capable of handling diverse tasks in unstructured environments, and also for improving the explainability and generalizability of AI systems.