Goal Generation

Goal generation in artificial intelligence focuses on enabling autonomous systems to autonomously define and pursue objectives, mirroring human learning and problem-solving. Current research emphasizes learning goal representations from demonstrations (e.g., using neural networks like DefGoalNet), generating goals from natural language descriptions (leveraging large language models), and creating more efficient learning curricula through methods like dynamical distance learning. These advancements are crucial for improving the sample efficiency and adaptability of reinforcement learning agents, leading to more robust and versatile robots and AI systems across diverse applications.