The Grasp Strategy of a Robot Passer Influences Performance and Quality of the Robot-Human Object Handover

Valerio Ortenzi*, Francesca Cini, Tommaso Pardi, Naresh Marturi, Rustam Stolkin, Peter Corke, Marco Controzzi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Task-aware robotic grasping is critical if robots are to successfully cooperate with humans. The choice of a grasp is multi-faceted; however, the task to perform primes this choice in terms of hand shaping and placement on the object. This grasping strategy is particularly important for a robot companion, as it can potentially hinder the success of the collaboration with humans. In this work, we investigate how different grasping strategies of a robot passer influence the performance and the perceptions of the interaction of a human receiver. Our findings suggest that a grasping strategy that accounts for the subsequent task of the receiver improves substantially the performance of the human receiver in executing the subsequent task. The time to complete the task is reduced by eliminating the need of a post-handover re-adjustment of the object. Furthermore, the human perceptions of the interaction improve when a task-oriented grasping strategy is adopted. The influence of the robotic grasp strategy increases as the constraints induced by the object's affordances become more restrictive. The results of this work can benefit the wider robotics community, with application ranging from industrial to household human-robot interaction for cooperative and collaborative object manipulation.

Original languageEnglish
Article number542406
JournalFrontiers in Robotics and AI
Publication statusPublished - 19 Oct 2020

Bibliographical note

Funding Information:
VO and MC initiated this work, oversaw, and advised the research. MC, FC, and VO designed the experiments. VO, NM, and TP built the experimental setup. VO, TP, FC, and MC performed the experiments. RS and MC provided the financial support. All authors contributed to the article and approved the submitted version.

Funding Information:
VO, NM, and RS were supported by the UK National Centre for Nuclear Robotics initiative, funded by EPSRC EP/R02572X/1. TP was supported by an NDA Ph.D. bursary. PC was supported by the Australian Research Council Centre of Excellence for Robotic Vision (project number CE140100016). FC and MC were supported by the European Commission under the Horizon 2020 framework program for Research and Innovation (project acronym: APRIL, project number: 870142).

Publisher Copyright:
© Copyright © 2020 Ortenzi, Cini, Pardi, Marturi, Stolkin, Corke and Controzzi.

Copyright 2020 Elsevier B.V., All rights reserved.


  • human-robot collaboration (HRC)
  • human-robot interaction (HRI)
  • object handover
  • seamless interaction
  • task-oriented grasping

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence


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