Task Transfer via Collaborative Manipulation for Insertion Assembly
Task Transfer via Collaborative Manipulation for Insertion Assembly
复制标题
通过协作操作进行插入组装的任务转移
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Billard
中科院分区：
文献类型：
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作者：
Klas Kronander;E. Burdet;A. Billard
插入任务是制造过程自动化的主要难点。这一类的基准问题，钉入孔，是在不确定的装配操作中出现的挑战的代表。即使在没有视觉信息的情况下，人类也能轻松地完成这项任务，依靠触觉和触觉感知来将钉子与洞对齐。没有一种通用的控制方法可以在机器人中复制这种能力。在这项工作中，我们提出了一个系统，允许通过协作任务执行将这项技能转移给机器人。机器人通过监测其姿势和感知力信息并将其编码为多元概率分布，从这些执行中学习。然后利用该分布实现主动校正策略，使机器人能够自主执行任务。通过五种不同自适应方案的孔内钉插入对比研究，验证了该方法的有效性。结果表明，与不进行自适应的盲随机搜索和插入相比，该学习模型具有显著的优势。
Insertion tasks are a major difficulty for automatizing manufacturing processes. The bench-mark problem in this category, peg-in-hole, is representative of the challenges that arise in uncertain assembly operations. Humans can carry out this task with ease, even in absence of visual information, relying on haptic and tactile sensing to align the peg with the hole. No generic control approach exist that can reproduce this capability in robots. In this work, we propose a system that allows to transfer this skill to a robot through collaborative task executions. The robot learns from these executions by monitoring its pose and sensed force information and encoding it as a multivariate probability distribution. This distribution is then used to realize an active correction strategy allowing the robot to execute the task autonomously. The approach is validated through a comparative study of peg-in-hole insertion using five different adaptation schemes. The results indicate a significant advantage of the learned model compared to blind random search and insertion without adaptation.