Literature DB >> 33137738

On the choice of grasp type and location when handing over an object.

F Cini1, V Ortenzi2, P Corke3, M Controzzi4.   

Abstract

The human hand is capable of performing countless grasps and gestures that are the basis for social activities. However, which grasps contribute the most to the manipulation skills needed during collaborative tasks, and thus which grasps should be included in a robot companion, is still an open issue. Here, we investigated grasp choice and hand placement on objects during a handover when subsequent tasks are performed by the receiver and when in-hand and bimanual manipulation are not allowed. Our findings suggest that, in this scenario, human passers favor precision grasps during such handovers. Passers also tend to grasp the purposive part of objects and leave "handles" unobstructed to the receivers. Intuitively, this choice allows receivers to comfortably perform subsequent tasks with the objects. In practice, many factors contribute to a choice of grasp, e.g., object and task constraints. However, not all of these factors have had enough emphasis in the implementation of grasping by robots, particularly the constraints introduced by a task, which are critical to the success of a handover. Successful robotic grasping is important if robots are to help humans with tasks. We believe that the results of this work can benefit the wider robotics community, with applications ranging from industrial cooperative manipulation to household collaborative manipulation.
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Entities:  

Year:  2019        PMID: 33137738     DOI: 10.1126/scirobotics.aau9757

Source DB:  PubMed          Journal:  Sci Robot        ISSN: 2470-9476


  8 in total

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

Authors:  Valerio Ortenzi; Francesca Cini; Tommaso Pardi; Naresh Marturi; Rustam Stolkin; Peter Corke; Marco Controzzi
Journal:  Front Robot AI       Date:  2020-10-19

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4.  Integrated linkage-driven dexterous anthropomorphic robotic hand.

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Journal:  Nat Commun       Date:  2021-12-14       Impact factor: 14.919

5.  Marine Robotics for Deep-Sea Specimen Collection: A Taxonomy of Underwater Manipulative Actions.

Authors:  Angela Mazzeo; Jacopo Aguzzi; Marcello Calisti; Simonepietro Canese; Michela Angiolillo; A Louise Allcock; Fabrizio Vecchi; Sergio Stefanni; Marco Controzzi
Journal:  Sensors (Basel)       Date:  2022-02-14       Impact factor: 3.576

6.  Application of an adapted FMEA framework for robot-inclusivity of built environments.

Authors:  Y J Ng; Matthew S K Yeo; Q B Ng; Michael Budig; M A Viraj J Muthugala; S M Bhagya P Samarakoon; R E Mohan
Journal:  Sci Rep       Date:  2022-03-01       Impact factor: 4.379

7.  Guidelines for Robot-to-Human Handshake From the Movement Nuances in Human-to-Human Handshake.

Authors:  John-John Cabibihan; Ahmed El-Noamany; Abdelrahman Mohamed Ragab; Marcelo H Ang
Journal:  Front Robot AI       Date:  2022-03-28

8.  Haptic object recognition based on shape relates to visual object recognition ability.

Authors:  Jason K Chow; Thomas J Palmeri; Isabel Gauthier
Journal:  Psychol Res       Date:  2021-08-05
  8 in total

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