[2203.11544] Visuo-Haptic Object Perception for Robots: An Overview Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2203.11544 (cs) [Submitted on 22 Mar 2022 ( v1 ), last revised 15 Mar 2023 (this version, v3)] Title: Visuo-Haptic Object Perception for Robots: An Overview Authors: Nicolás Navarro-Guerrero , Sibel Toprak , Josip Josifovski , Lorenzo Jamone View a PDF of the paper titled Visuo-Haptic Object Perception for Robots: An Overview, by Nicol\'as Navarro-Guerrero and 3 other authors View PDF HTML (experimental) Abstract: The object perception capabilities of humans are impressive, and this becomes even more evident when trying to develop solutions with a similar proficiency in autonomous robots. While there have been notable advancements in the technologies for artificial vision and touch, the effective integration of these two sensory modalities in robotic applications still needs to be improved, and several open challenges exist. Taking inspiration from how humans combine visual and haptic perception to perceive object properties and drive the execution of manual tasks, this article summarises the current state of the art of visuo-haptic object perception in robots. Firstly, the biological basis of human multimodal object perception is outlined. Then, the latest advances in sensing technologies and data collection strategies for robots are discussed. Next, an overview of the main computational techniques is presented, highlighting the main challenges of multimodal machine learning and presenting a few representative articles in the areas of robotic object recognition, peripersonal space representation and manipulation. Finally, informed by the latest advancements and open challenges, this article outlines promising new research directions. Comments: published in Autonomous Robots Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2203.11544 [cs.RO] (or arXiv:2203.11544v3 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2203.11544 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Autonomous Robots, 27 (2023) https://link.springer.com/article/10.1007/s10514-023-10091-y Related DOI : https://doi.org/10.1007/s10514-023-10091-y Focus to learn more DOI(s) linking to related resources Submission history From: Nicolás Navarro-Guerrero [ view email ] [v1] Tue, 22 Mar 2022 08:55:36 UTC (5,271 KB) [v2] Tue, 16 Aug 2022 13:30:32 UTC (1,953 KB) [v3] Wed, 15 Mar 2023 15:41:27 UTC (1,988 KB) Full-text links: Access Paper: View a PDF of the paper titled Visuo-Haptic Object Perception for Robots: An Overview, by Nicol\'as Navarro-Guerrero and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2022-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from