Data-Oriented State Space Discretization for Crowdsourced Robot Learning of Physical Skills
Data-Oriented State Space Discretization for Crowdsourced Robot Learning of Physical Skills
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DOI：
10.1115/1.4047961
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Leidi Zhao;Lu Lu-Lu;Cong Wang
中科院分区：
文献类型：
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作者：
Leidi Zhao;Lu Lu-Lu;Cong Wang
这项工作讨论了机器人物理智能的众包学习方案。该计划利用来自众包导师的大量数据，允许机器人合成从未展示或仅部分展示的新身体技能，而无需进行大量的重新训练。该学习计划采用数据管理方法，可持续地管理不断收集的数据和不断增长的知识库。该方法是使用模拟的挑战，解决一个瓶子的难题进行验证。该学习方案旨在实现无处不在的机器人学习身体技能，并有可能自动化许多目前很难自动化的高要求的任务。
This work discusses a crowdsourced learning scheme for robot physical intelligence. Using a large amount of data from crowdsourced mentors, the scheme allows robots to synthesize new physical skills that are never demonstrated or only partially demonstrated without heavy re-training. The learning scheme features a data management method to sustainably manage continuously collected data and a growing knowledge library. The method is validated using a simulated challenge of solving a bottle puzzle. The learning scheme aims at realizing ubiquitous robot learning of physical skills and has the potential of automating many demanding tasks that are currently hard to robotize.