Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
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DOI：
10.1109/iros.2018.8593624
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
M. A. Rana;Mustafa Mukadam;S. Ahmadzadeh;S. Chernova;Byron Boots
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文献类型：
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作者：
M. A. Rana;Mustafa Mukadam;S. Ahmadzadeh;S. Chernova;Byron Boots
从演示中学习（LfD）是一种流行的方法，无需手动编程即可赋予机器人技能。通常，LfD 依赖于在整洁的环境中进行人类演示。这可以防止演示受到不相关对象的影响，这些对象的影响可能会混淆人类的真实意图或所需技能的限制。然而，假设机器人的环境在捕捉人类演示时总是可以重组以消除混乱，这是不现实的。为了解决这个问题，我们基于最近基于推理的技能表示和再现技术，开发了一种重要性加权批量和增量技能学习方法。我们的方法减少了环境对所学技能的不良影响，同时仍然捕捉到显着的人类行为。我们提供了我们的方法的批量和增量版本，并在具有到达和放置技能的 7 自由度 JACO2 机械臂上验证了我们的算法。
Learning from Demonstration (LfD) is a popular approach to endowing robots with skills without having to program them by hand. Typically, LfD relies on human demonstrations in clutter-free environments. This prevents the demonstrations from being affected by irrelevant objects, whose influence can obfuscate the true intention of the human or the constraints of the desired skill. However, it is unrealistic to assume that the robot's environment can always be restructured to remove clutter when capturing human demonstrations. To contend with this problem, we develop an importance weighted batch and incremental skill learning approach, building on a recent inference-based technique for skill representation and reproduction. Our approach reduces unwanted environmental influences on the learned skill, while still capturing the salient human behavior. We provide both batch and incremental versions of our approach and validate our algorithms on a 7-DOF JACO2 manipulator with reaching and placing skills.