Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

Alessandro Carfì*, Timothy Patten, Yingyi Kuang, Ali Hammoud, Mohamad Alameh, Elisa Maiettini, Abraham Itzhak Weinberg, Diego Faria, Fulvio Mastrogiovanni, Guillem Alenyà, Lorenzo Natale, Véronique Perdereau, Markus Vincze, Aude Billard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Human-object interaction is of great relevance for robots to operate in human environments. However, state-of-the-art robotic hands are far from replicating humans skills. It is, therefore, essential to study how humans use their hands to develop similar robotic capabilities. This article presents a deep dive into hand-object interaction and human demonstrations, highlighting the main challenges in this research area and suggesting desirable future developments. To this extent, the article presents a general definition of the hand-object interaction problem together with a concise review for each of the main subproblems involved, namely: sensing, perception, and learning. Furthermore, the article discusses the interplay between these subproblems and describes how their interaction in learning from demonstration contributes to the success of robot manipulation. In this way, the article provides a broad overview of the interdisciplinary approaches necessary for a robotic system to learn new manipulation skills by observing human behavior in the real world.
Original languageEnglish
Article number714023
JournalFrontiers in Robotics and AI
Publication statusPublished - 1 Oct 2021

Bibliographical note

Copyright © 2021 Carfì, Patten, Kuang, Hammoud, Alameh, Maiettini, Weinberg, Faria, Mastrogiovanni, Alenyà, Natale, Perdereau, Vincze and Billard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Funding: This work is supported by the CHIST-ERA (2014-2020) project InDex and received funding from the Italian Ministry of Education and Research (MIUR), Austrian Science Fund (FWF) under grant agreement No. I3969-N30, Engineering and Physical Sciences Research Council (EPSRC-UK) with reference EP/S032355/1 and Agence Nationale de la Recherche (ANR) under grant agreement No. ANR-18-CHR3-0004. This work is also supported by the ERA-net CHIST-ERA project BURG (PCIN2019-103447).


  • Robotics and AI
  • hand-object interaction
  • learning from demonstration
  • imitation learning
  • transfer learning
  • grasping
  • manipulation
  • anthropomorphic hands
  • data extraction


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