A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control
A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control
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机器人操作框架：技能形式主义、元学习和自适应控制
DOI：
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发表时间：
2018
期刊：
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通讯作者：
S. Haddadin
中科院分区：
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作者：
Lars Johannsmeier;Malkin Gerchow;S. Haddadin
在本文中，我们介绍了一种用于表达和学习力敏感机器人操作技能的新颖框架。它基于一种形式主义，通过元参数学习和兼容的技能规范扩展了我们之前在自适应阻抗控制方面的工作。这样，系统还能够通过合并流程描述和质量评估指标来利用抽象的专家知识。我们评估了各种最先进的元参数学习方案，并通过实验比较选定的方案。我们的结果清楚地表明，我们的自适应阻抗控制器与精心定义的技能形式相结合，显着降低了操作任务的复杂性，即使对于学习具有亚毫米工业公差的孔中钉也是如此。总体而言，所考虑的系统能够在 20 分钟内学习该技能的各种变化。事实上，通过实验，该系统能够在没有视觉反馈的情况下比人类更快地执行学习任务，从而在如此真实的性能下实现了第一个基于学习的复杂装配解决方案。
In this paper we introduce a novel framework for expressing and learning force-sensitive robot manipulation skills. It is based on a formalism that extends our previous work on adaptive impedance control with meta parameter learning and compatible skill specifications. This way the system is also able to make use of abstract expert knowledge by incorporating process descriptions and quality evaluation metrics. We evaluate various state-of-the-art schemes for meta parameter learning and experimentally compare selected ones. Our results clearly indicate that the combination of our adaptive impedance controller with a carefully defined skill formalism significantly reduces the complexity of manipulation tasks even for learning peg-in-hole with submillimeter industrial tolerances. Overall, the considered system is able to learn variations of this skill in under 20 minutes. In fact, experimentally the system was able to perform the learned tasks without visual feedback faster than humans, leading to the first learning-based solution of complex assembly at such real-world performance.