Robot Learning

Robot learning aims to enable robots to acquire new skills and adapt to diverse environments through learning, rather than explicit programming. Current research heavily focuses on improving data efficiency and generalization, employing techniques like transformer networks, diffusion models, and reinforcement learning algorithms (e.g., PPO, SAC) often combined with large language models and imitation learning from human demonstrations or simulations. This field is crucial for advancing robotics, enabling robots to perform complex tasks in unstructured settings and potentially revolutionizing various industries, from manufacturing and healthcare to logistics and home assistance.