EDITORIAL article
Front. Robot. AI
Sec. Computational Intelligence in Robotics
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1662674
This article is part of the Research TopicMerging Symbolic and Data-Driven AI for Robot AutonomyView all 11 articles
Editorial: Merging symbolic and data-driven AI for robot autonomy
Provisionally accepted
- 1Universita degli Studi di Verona Dipartimento di Informatica, Verona, Italy
- 2The University of Edinburgh School of Informatics, Edinburgh, United Kingdom
- 3Universita della Calabria Dipartimento di Matematica e Informatica, Arcavacata di Rende, Italy
- 4National Centre for Scientific Research "Demokritos", Agia Paraskevi, Greece
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Robots are increasingly being deployed to assist humans in many applications such as medicine, 11 navigation, and industrial automation. To truly collaborate with humans in complex 12 environments, robots require advanced cognitive capabilities, including the ability to reason with 13 domain-specific commonsense knowledge and the noisy observations obtained in the presence of 14 partial observability and non-deterministic action outcomes. In summary, the contributions to this topic highlight the importance of merging symbolic and 80 data-driven AI methods in the context of robotics (and AI). These papers demonstrate how such 81 hybrid frameworks enable robots to reason with complex cognitive theories and noisy 82 multimodal sensor observations to achieve reliable, efficient, and transparent scene 83 understanding, planning, diagnostics, and human-robot collaboration in complex simulated and 84
Keywords: Neurosymbolic AI, Probabilistic reasoning, Reasoning under uncertainty, Hybrid 9 AI, Robotics 10
Received: 09 Jul 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 Meli, Sridharan, Perri and Katzouris. 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) or licensor 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.
* Correspondence: Daniele Meli, Universita degli Studi di Verona Dipartimento di Informatica, Verona, Italy
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