ICRA 2020poster3 citations

TACTO-Selector: Enhanced Hierarchical Fusion of PBVS with Reactive Skin Control for Physical Human-Robot Interaction

Ana Elvira H. Martin, Emmanuel Dean-Leon, Gordon Cheng

Abstract

In a physical Human-Robot Interaction for industrial scenarios is paramount to guarantee the safety of the user while keeping the robot's performance. Hierarchical task approaches are not sufficient since they tend to sacrifice the low priority tasks in order to guarantee the consistency of the main task. To handle this problem, we enhance the standard hierarchical fusion by introducing a novel interactive task-reconfiguring approach (TACTO-Selector) that uses the information of the tactile interaction to adapt the dimension of the tasks, therefore guaranteeing the execution of the safety task while performing the other task as good as possible. In this work, we hierarchically combine a 6 DOF Position-Based Visual Servoing (PBVS) task with a reactive skin control. This approach was evaluated on a 6 DOF industrial robot showing an improvement of 36.37% on average in tracking error reduction compared with a standard approach.

BibTeX
@inproceedings{icra2020_tactoselectorenh,
  title = {TACTO-Selector: Enhanced Hierarchical Fusion of PBVS with Reactive Skin Control for Physical Human-Robot Interaction},
  author = {Ana Elvira H. Martin and Emmanuel Dean-Leon and Gordon Cheng},
  booktitle = {ICRA 2020},
  year = {2020}
}