A robotic behavior model that can reliably generate behaviors from natural language inputs in real time would substantially expedite the adoption of industrial robots due to enhanced system flexibility. To facilitate these efforts, we construct a framework in which learned behaviors, created by a natural language abstractor, are verifiable by construction. Leveraging recent advancements in motion primitives and probabilistic verification, we construct a natural-language behavior abstractor that generates behaviors by synthesizing a directed graph over the provided motion primitives. If these component motion primitives are constructed according to the criteria we specify, the resulting behaviors are probabilistically verifiable. We demonstrate this verifiable behavior generation capacity in both simulation on an exploration task and on hardware with a robot scooping granular media.

翻译：一种能实时根据自然语言输入可靠生成行为的机器人行为模型，将因系统灵活性的提升而显著加速工业机器人的应用。为推进相关工作，我们构建了一个框架，在该框架中由自然语言抽象器创建的学习行为可通过构造方式实现可验证性。利用运动基元和概率验证的最新进展，我们构建了一个自然语言行为抽象器，该抽象器通过将运动基元组合为有向图来生成行为。若这些组件运动基元按照我们指定的准则构建，则生成的行为可进行概率验证。我们在探索任务的仿真环境和颗粒介质铲取的硬件实验中，均验证了这种可验证行为生成能力。