In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors. With Q-Rock, we suggest a novel, integrative approach to automate robot development processes. Q-Rock combines several machine learning and reasoning techniques to deal with the increasing complexity in the design of robotic systems. The Q-Rock development cycle consists of three complementary processes: (1) automated exploration of capabilities that a given robotic hardware provides, (2) classification and semantic annotation of these capabilities to generate more complex behaviors, and (3) mapping between application requirements and available behaviors. These processes are based on a graph-based representation of a robot’s structure, including hardware and software components. A central, scalable knowledge base enables collaboration of robot designers including mechanical, electrical and systems engineers, software developers and machine learning experts. In this paper we formalize Q-Rock’s integrative development cycle and highlight its benefits with a proof-of-concept implementation and a use case demonstration.

中文翻译：

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机器人行为自动自我探索的开发周期

在本文中，我们介绍了 Q-Rock，这是一个用于机器人行为的自动自我探索和鉴定的开发周期。通过 Q-Rock，我们建议采用一种新颖的集成方法来自动化机器人开发过程。Q-Rock 结合了多种机器学习和推理技术，以应对机器人系统设计中日益复杂的问题。Q-Rock 开发周期由三个互补的过程组成：(1) 自动探索给定机器人硬件提供的功能，(2) 这些功能的分类和语义注释以生成更复杂的行为，以及 (3) 应用程序需求之间的映射和可用的行为。这些过程基于机器人结构的基于图形的表示，包括硬件和软件组件。一个中央，可扩展的知识库支持包括机械、电气和系统工程师、软件开发人员和机器学习专家在内的机器人设计师之间的协作。在本文中，我们将 Q-Rock 的集成开发周期形式化，并通过概念验证实施和用例演示来强调其优势。