Developing machine intelligence abilities in robots and autonomous systems is an expensive and time consuming process. Existing solutions are tailored to specific applications and are harder to generalize. Furthermore, scarcity of training data adds a layer of complexity in deploying deep machine learning models. We present a new platform for General Robot Intelligence Development (GRID) to address both of these issues. The platform enables robots to learn, compose and adapt skills to their physical capabilities, environmental constraints and goals. The platform addresses AI problems in robotics via foundation models that know the physical world. GRID is designed from the ground up to be extensible to accommodate new types of robots, vehicles, hardware platforms and software protocols. In addition, the modular design enables various deep ML components and existing foundation models to be easily usable in a wider variety of robot-centric problems. We demonstrate the platform in various aerial robotics scenarios and demonstrate how the platform dramatically accelerates development of machine intelligent robots.

翻译：开发机器人和自主系统的机器智能是一项昂贵且耗时的过程。现有解决方案针对特定应用定制，难以通用化。此外，训练数据的稀缺性为部署深度机器学习模型增加了复杂性。我们提出了一种名为通用机器人智能开发平台（GRID）的新平台，以解决这两个问题。该平台使机器人能够学习、组合并适应其物理能力、环境约束和目标所需的技能。该平台通过理解物理世界的基础模型，解决了机器人领域的AI问题。GRID从底层设计为可扩展，以兼容新型机器人、车辆、硬件平台及软件协议。此外，其模块化设计使各种深度机器学习组件和现有基础模型能够轻松应用于更广泛的机器人中心问题。我们在多种空中机器人场景中展示了该平台，并证明了它如何显著加速机器智能机器人的开发。