Symmetry and Asymmetry in Machine Learning and Data Science
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 25 April 2026 | Viewed by 14
Special Issue Editors
Interests: intelligent cognitive technology of marine environment; lightweight deep learning and intelligent computing of marine unmanned systems; machine learning
Special Issue Information
Dear Colleagues,
The application of machine learning (ML) in robotics and autonomous systems has enabled significant advancements, yet challenges remain in addressing the structural complexities of real-world operational environments. Symmetry and asymmetry—observed in sensor data, system dynamics, or task requirements—represent critical but understudied properties in these fields. For example, symmetry in multi-sensor alignment (e.g., LiDAR-camera calibration) can improve the reliability of perception systems, while asymmetry under environmental conditions (e.g., uneven terrain for unmanned ground vehicles or occlusions in autonomous driving scenarios) necessitates adaptive control frameworks. Current ML methodologies, however, often fail to systematically incorporate these structural properties, which may limit their effectiveness in real-time decision making, energy optimization, and safety-critical interpretability.
This Special Issue, "Symmetry and Asymmetry in Machine Learning and Data Science", aligns with the journal’s focus on AI and pattern recognition in automation. It seeks to advance methodologies that integrate geometric priors, data-driven learning, and domain-specific constraints to enhance the robustness and scalability of robotic and autonomous systems.
Suggested themes include (but are not limited to) the following:
- Symmetry-aware perception: Invariant feature learning for multi-sensor fusion, 3D object detection, and SLAM under dynamic conditions;
- Asymmetric learning: Strategies for handling imbalanced data in robotic vision or fault-tolerant control under partial observability;
- Geometric deep learning: Graph-based architectures for swarm robotics coordination and human–robot collaboration;
- Adaptive control: Symmetry-breaking mechanisms for agile robotic mobility (e.g., aerial drones, legged robots) or energy-aware path planning;
- Explainable autonomy: Model interpretability for safety-critical verification and human–AI interaction.
Submissions addressing theoretical advancements, algorithmic innovations, or practical implementations in industrial, service, or field robotics are welcomed. We encourage researchers to contribute to this collaborative effort to advance the integration of structural principles in intelligent systems.
Dr. Bo Wang
Dr. Xiaotian Li
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- symmetric feature learning
- asymmetric data processing
- geometric deep learning
- imbalanced learning in robotics
- sensor fusion for autonomous systems
- dynamic environment adaptation
- explainable ai in autonomous systems
- adaptive control algorithms
- swarm robotics coordination
- human–robot interaction
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