What applications is AI ready for? Advances in deep learning and generative approaches have produced AIs that learn from massive online data and outperform manually built AIs. Some of these AIs outperform people. It is easy (but misleading) to conclude that today's AI technologies are learning to do anything and everything. Conversely, it is striking that big data, deep learning, and generative AI have had so little impact on robotics. For example, today's autonomous robots do not learn to provide home care or to be nursing assistants. Current robot applications are created using manual programming, mathematical models, planning frameworks, and reinforcement learning. These methods do not lead to the leaps in performance and generality seen with deep learning and generative AI. Better approaches to train robots for service applications would greatly expand their social roles and economic impact. AI research is now extending "big data" approaches to train robots by combining multimodal sensing and effector technology from robotics with deep learning technology adapted for embodied systems. These approaches create robotic (or "experiential") foundation models (FMs) for AIs that perceive and act in the world. Robotic FM approaches differ in their expectations, sources, and timing of training data. Like mainstream FM approaches, some robotic FM approaches use vast data to create adult expert-level robots. In contrast, developmental robotic approaches would create progressive FMs that learn continuously and experientially. Aspirationally, these would progress from child-level to student-level, apprentice-level, and expert levels. They would acquire self-developed and socially developed competences. These AIs would model the goals of people around them. Like people, they would learn to coordinate, communicate, and collaborate.

翻译：人工智能准备好应用于哪些场景？深度学习和生成式方法的进步催生了能够从海量在线数据中学习、且性能优于人工构建模型的AI系统，其中部分AI甚至超越了人类。人们很容易（但具有误导性）得出结论：当今AI技术正在学习如何胜任一切任务。然而引人注目的是，大数据、深度学习和生成式AI对机器人领域的影响微乎其微。例如，当前的自主机器人并未学会提供居家护理或担任护理助理。现有的机器人应用仍依赖手工编程、数学模型、规划框架和强化学习构建。这些方法未能带来深度学习和生成式AI所展现的性能与通用性飞跃。为服务型应用开发更优的机器人训练方法，将极大扩展其社会角色与经济影响。目前AI研究正通过将机器人领域的多模态传感与执行器技术，与适配具身系统的深度学习技术相结合，将"大数据"方法拓展至机器人训练。这些方法为在真实世界中感知与行动的AI创建了机器人（或称"经验型"）基础模型（FMs）。机器人基础模型方法在训练数据的预期、来源和时间安排上各有差异。与主流基础模型方法类似，部分机器人基础模型方法利用海量数据打造具备成人专家水准的机器人。相比之下，发展型机器人方法则致力于创建渐进式基础模型，使其能够持续进行经验式学习。理想状态下，这些模型将从儿童水平逐步提升至学生、学徒乃至专家水平，并自主获取与社会共同发展的能力。这类AI将为周围人类的行为目标建模，并像人类一样学习协调、沟通与协作。