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An approach to robot task learning and planning with loops

机译:带有循环的机器人任务学习和计划方法

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This paper addresses robot task model learning and planning with loops. By detecting and modeling loops in solved tasks it is possible to learn and solve wider classes of problems. We extend our previous work on experience-based planning domains in robotics to detect, represent and generate loops in action sequences. This approach provides methods for, (i) conceptualizing robot experiences possibly containing loops and learning high-level robot activity schemata with loops; and (ii) instantiating schemata with loops for solving problem instances of the same task with varying sets of objects. Demonstrations of this system in both real and simulated environments prove its potentialities.
机译:本文介绍了机器人任务模型的学习和循环规划。通过检测和建模已解决任务中的循环,可以学习和解决更广泛的问题类别。我们扩展了以前在机器人技术中基于经验的计划领域的工作,以检测,表示并生成动作序列中的循环。这种方法提供了以下方法:(i)概念化可能包含循环的机器人体验,并通过循环学习高级机器人活动图式; (ii)使用循环实例化模式,以解决具有不同对象集的同一任务的问题实例。在真实和模拟环境中对该系统的演示都证明了其潜力。

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