A task-sequencing simulator in robotics manipulation to integrate simulation-for-learning and simulation-for-execution is introduced. Unlike existing machine-learning simulation where a non-decomposed simulation is used to simulate a training scenario, the task-sequencing simulator runs a composed simulation using building blocks. This way, the simulation-for-learning is structured similarly to a multi-step simulation-for-execution. To compose both learning and execution scenarios, a unified trainable-and-composable description of blocks called a concept model is proposed and used. Using the simulator design and concept models, a reusable simulator for learning different tasks, a common-ground system for learning-to-execution, simulation-to-real is achieved and shown.
翻译:在机器人操作中采用任务序列模拟器,将模拟学习和模拟执行结合起来。与现有的机器学习模拟器不同,利用现有的机器学习模拟器,使用非分解模拟器模拟培训情景,任务序列模拟器使用构件进行合成模拟。这样,模拟学习的结构与多步模拟执行相似。为了结合学习和执行设想方案,建议并使用一个统一的、可培训的和可操作的块块描述,称为概念模型。使用模拟器设计和概念模型,一个用于学习不同任务的可重复使用的模拟器,一个用于学习到执行的通用地面系统,模拟到现实的实现和显示。