Learning Real-time Closed Loop Robotic Reaching from Monocular Vision by Exploiting A Control Lyapunov Function Structure

Zheyu Zhuang; Jürgen Leitner; Robert Mahony

IROS 2019poster3 citations

Learning Real-time Closed Loop Robotic Reaching from Monocular Vision by Exploiting A Control Lyapunov Function Structure

Zheyu Zhuang, Jürgen Leitner, Robert Mahony

Abstract

Visual reaching and grasping is a fundamental problem in robotics research. This paper proposes a novel approach based on deep learning a control Lyapunov function and its derivatives by encouraging a differential constraint in addition to vanilla regression that directly regresses independent joint control inputs. A key advantage of the proposed approach is that an estimate of the value of the control Lyapunov function is available in real-time that can be used to monitor the system performance and provide a level of assurance concerning progress towards the goal. The results we obtain demonstrate that the proposed approach is more robust and more reliable than vanilla regression.

BibTeX

@inproceedings{iros2019_learningrealtime,
  title = {Learning Real-time Closed Loop Robotic Reaching from Monocular Vision by Exploiting A Control Lyapunov Function Structure},
  author = {Zheyu Zhuang and Jürgen Leitner and Robert Mahony},
  booktitle = {IROS 2019},
  year = {2019}
}