Active Extrinsic Contact Sensing: Application to General Peg-in-Hole Insertion
Active Extrinsic Contact Sensing: Application to General Peg-in-Hole Insertion
复制标题
主动外部接触传感：在一般孔内插入中的应用
DOI：
10.1109/icra46639.2022.9812017
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Alberto Rodriguez
中科院分区：
文献类型：
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作者：
Sangwoon Kim;Alberto Rodriguez
我们提出一种方法，该方法能主动估计被抓取的刚性物体与其环境之间的接触位置，并将其作为轴孔插入策略的输入。一个估计模型和一个主动触觉反馈控制器协同工作，以准确估计外部接触。控制器通过调节一致的接触模式来帮助估计模型获得更好的估计。更好的估计使控制器更容易调节接触。然后，我们训练一个与物体无关的插入策略，该策略学习使用一系列接触估计来引导一个未见过的销钉插入孔中。与之前直接从触觉信号学习策略的工作不同，由于该策略处于接触配置空间，它可以在模拟中直接学习。最后，我们在一个真实实验中一起演示和评估主动外部接触线估计和经过训练的插入策略。我们表明，与之前使用端到端方法的工作相比，所提出的方法能以更高的成功率和更少的插入尝试次数插入各种形状的测试物体。查看补充视频和结果，请访问https://sites.google.com/view/active - extrinsic - contact。
We propose a method that actively estimates contact location between a grasped rigid object and its environment and uses this as input to a peg-in-hole insertion policy. An estimation model and an active tactile feedback controller work collaboratively to estimate the external contacts accurately. The controller helps the estimation model get a better estimate by regulating a consistent contact mode. The better estimation makes it easier for the controller to regulate the contact. We then train an object-agnostic insertion policy that learns to use the series of contact estimates to guide the insertion of an unseen peg into a hole. In contrast with previous works that learn a policy directly from tactile signals, since this policy is in contact configuration space, it can be learned directly in simulation. Lastly, we demonstrate and evaluate the active extrinsic contact line estimation and the trained insertion policy together in a real experiment. We show that the proposed method inserts various-shaped test objects with higher success rates and fewer insertion attempts than previous work with end-to-end approaches. See supplementary video and results at https://sites.google.com/view/active-extrinsic-contact.