Identifying Precursors to Failures in Robotic Lift-and-Place Tasks

Zeyu Shangguan, Rajas Chitale, Rutvik Patel, Satyandra Gupta, Daniel Seita

Keywords: Failure prediction, Robotics

TL;DR: This paper research proactive failure prediction and prevention.

Abstract:

Failure prediction for robotic manipulation in in-
dustrial applications has experienced substantial advancements in
terms of efficiency and reliability driven by the latest innovations
in machine learning. Most of the existing works focus on reactive
failure prediction. As an offline analyzing technique, it is not
suitable for real-time failure prevention. Therefore, proactive
failure prediction becomes a valuable approach to meeting the
requirements of online deployment. Although recent research on
proactive failure prediction has made progress, it still suffers
from limitations such as object specificity, fixed action spaces,
and high system complexity. In this paper, we study lift-and-
place tasks, and propose a more effective approach to identifying
failure by analyzing the relative motion of target objects in the
scene. Consequently, we propose a novel method that proactively
predict the failure by embedding the relative motions in the scene.
We verify our proposed method on both simulation and real-
world data. The experimental results indicate that our proposed
method not only demonstrates improved generalization but also
provides a more precise response to the precursors of failure.

Submission Number: 30