One-shot learning for autonomous aerial manipulation

Claudio Zito*, Eliseo Ferrante

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This paper is concerned with learning transferable contact models for aerial manipulation tasks. We investigate a contact-based approach for enabling unmanned aerial vehicles with cable-suspended passive grippers to compute the attach points on novel payloads for aerial transportation. This is the first time that the problem of autonomously generating contact points for such tasks has been investigated. Our approach builds on the underpinning idea that we can learn a probability density of contacts over objects’ surfaces from a single demonstration. We enhance this formulation for encoding aerial transportation tasks while maintaining the one-shot learning paradigm without handcrafting task-dependent features or employing ad-hoc heuristics; the only prior is extrapolated directly from a single demonstration. Our models only rely on the geometrical properties of the payloads computed from a point cloud, and they are robust to partial views. The effectiveness of our approach is evaluated in simulation, in which one or three quadcopters are requested to transport previously unseen payloads along a desired trajectory. The contact points and the quadcopters configurations are computed on-the-fly for each test by our approach and compared with a baseline method, a modified grasp learning algorithm from the literature. Empirical experiments show that the contacts generated by our approach yield a better controllability of the payload for a transportation task. We conclude this paper with a discussion on the strengths and limitations of the presented idea, and our suggested future research directions.

Original languageEnglish
Article number960571
Pages (from-to)1-13
Number of pages13
JournalFrontiers in Robotics and AI
Volume9
DOIs
Publication statusPublished - 5 Oct 2022

Bibliographical note

Funding Information:
We thank Giuseppe Loianno and Marek Kopicki for the support in developing this work.

Publisher Copyright:
Copyright © 2022 Zito and Ferrante.

Keywords

  • aerial grasping
  • aerial manipulation
  • one-shot learning
  • robotics
  • single and collaborative transportation

Fingerprint

Dive into the research topics of 'One-shot learning for autonomous aerial manipulation'. Together they form a unique fingerprint.

Cite this