Training Robot

Training robots efficiently and effectively is a major focus of robotics research, aiming to enable robots to learn complex tasks with minimal human intervention. Current approaches leverage reinforcement learning, often augmented by techniques like interactive learning, imitation learning (including human demonstrations via augmented reality), and visual saliency to improve sample efficiency and generalization. These methods, frequently employing neural networks such as convolutional neural networks and transformers, are being applied to diverse tasks ranging from warehouse logistics to assistive robotics, with the goal of creating more adaptable and robust robotic systems for various applications.