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The Future of Robotics: AI Algorithms, Ethics, and Real-World Applications
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The Future of Robotics: AI Algorithms, Ethics, and Real-World Applications

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI in Autonomous Systems".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 2856

Special Issue Editors

Dr. Ergina Kavallieratou

E-Mail Website
Guest Editor
Department of Information & Communication Systems Engineering, University of the Aegean, Mytilene, Greece
Interests: image processing; computer vision; document image processing; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Nikolaos Vasilopoulos

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Guest Editor
Department of Performing and Digital Arts, University of the Peloponnese, Tripoli, Greece
Interests: digital image processing; algorithmic music composition
Dr. Paraskevas Diamantatos

E-Mail Website
Guest Editor
Department of Information & Communication Systems Engineering, University of the Aegean, Mytilene, Greece
Interests: computer vision; artificial intelligence; robotics; graphics; information & communication systems security

Special Issue Information

Dear Colleagues,

Robotics is entering a new era, driven by rapid advances in artificial intelligence that enable robots to perceive, reason, learn, and act with increasing autonomy and sophistication. This Special Issue, The Future of Robotics: AI Algorithms, Ethics, and Real-World Applications, aims to show recent breakthroughs and ongoing challenges at the intersection of AI and robotics.

Contributions are invited that address novel AI algorithms for perception, decision-making, planning, and control in robotic systems; innovative applications of intelligent robots in sectors such as healthcare, manufacturing, logistics, agriculture, daily life and research on the ethical, legal, and societal implications of deploying AI-driven robots. The Issue particularly encourages works that provide insight into responsible innovation, transparency, fairness, and the human-centered design of robotic systems.

By collecting theoretical advances, practical implementations, and discussions of regulatory and ethical frameworks, this Special Issue seeks to serve as a comprehensive resource for researchers, practitioners, and policymakers, navigating the rapidly evolving landscape of intelligent robotics.

This Special Issue aims to explore the transformative impact of artificial intelligence (AI) algorithms on the future of robotics, with an emphasis on ethical considerations and real-world applications. By bringing together cutting-edge research and perspectives from academia and industry, this Issue seeks to illuminate how AI is redefining the capabilities, roles, and responsibilities of robotic systems in contemporary society.

In this Special Issue, original research articles and reviews are welcome.

Research areas may include (but are not limited to) the following:

  • Machine learning for control and optimization;
  • Autonomous robotics and intelligent agents;
  • Computer vision in automated environments;
  • Sensor fusion and data-driven systems;
  • Digital twins and cyber-physical systems;
  • Predictive maintenance and fault diagnosis;
  • Adaptive manufacturing and smart production;
  • Intelligent logistics and supply chain automation;
  • Human-machine interaction and collaboration;
  • Ethical, legal, and societal implications of AI-driven automation.

We look forward to receiving your contributions.

Dr. Ergina Kavallieratou
Dr. Nikolaos Vasilopoulos
Dr. Paraskevas Diamantatos
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 250 words) can be sent to the Editorial Office for assessment.

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. AI is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • automation systems
  • machine learning
  • intelligent control
  • autonomous robotics
  • computer vision
  • sensor fusion
  • digital twins
  • predictive maintenance
  • human-machine interaction

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 3662 KB  
Article
Evaluating the Perception, Understanding, and Forgetting of Progressive Neural Networks: A Quantitative and Qualitative Analysis
by Lucía Güitta-López, Jaime Boal and Álvaro J. López-López
AI 2026, 7(4), 120; https://doi.org/10.3390/ai7040120 - 31 Mar 2026
Viewed by 664
Abstract
The use of virtual environments to collect the experience required by deep reinforcement learning models is accelerating the deployment of these algorithms in industrial environments. However, once the experience-gathering problem is solved, it is necessary to address how to efficiently transfer the knowledge [...] Read more.
The use of virtual environments to collect the experience required by deep reinforcement learning models is accelerating the deployment of these algorithms in industrial environments. However, once the experience-gathering problem is solved, it is necessary to address how to efficiently transfer the knowledge from the virtual scenario to reality. This paper focuses on examining Progressive Neural Networks (PNNs) as a promising transfer learning technique. The analyses carried out range from studying the capabilities and limits of the layers responsible for learning the state representation from a pixel space, which could arguably be the convolutional blocks, to the forgetting agents suffer when learning a new task. Introducing controlled visual changes in the environment scene can lead to a performance degradation of 50.3% in the worst-case scenario. These visual discrepancies significantly impact the agent’s learning time and accuracy when using a PNN architecture. Regarding the PNN forgetting assessment, partial forgetting occurs in two of the three environments analyzed, those where the agent masters its new task. This could be due to a balance between the relevance of the new features learned and the ones inherited from the teacher agent. Full article
(This article belongs to the Special Issue The Future of Robotics: AI Algorithms, Ethics, and Real-World Applications)
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16 pages, 19425 KB  
Article
Learning and Reconstruction of Mobile Robot Trajectories with LSTM Autoencoders: A Data-Driven Framework for Real-World Deployment
by Jakub Krejčí, Marek Babiuch, Václav Krys and Zdenko Bobovský
AI 2025, 6(12), 302; https://doi.org/10.3390/ai6120302 - 24 Nov 2025
Viewed by 1403
Abstract
Accurate trajectory learning and reconstruction represent a core challenge in mobile robotics, particularly in environments affected by sensor noise, drift, and incomplete data. Addressing this challenge is essential for reliable navigation and motion control in real-world Internet of Robotic Things (IoRT) systems. This [...] Read more.
Accurate trajectory learning and reconstruction represent a core challenge in mobile robotics, particularly in environments affected by sensor noise, drift, and incomplete data. Addressing this challenge is essential for reliable navigation and motion control in real-world Internet of Robotic Things (IoRT) systems. This paper presents a data-driven framework for learning and reconstructing mobile robot trajectories using LSTM autoencoders. Trajectory data were collected from both simulation and real-world experiments with a Unitree GO1 quadruped robot, preprocessed through normalization, sequence padding, and trajectory boundary flags, and then used to train recurrent neural network models. The proposed architecture employs bidirectional LSTM layers and a custom loss function combining reconstruction, velocity, and boundary terms to improve trajectory stability. Experimental results show stable reconstruction accuracy across simulated and real-world datasets, with the position RMSE reduced from 0.92 m to 0.60 m and the yaw MAE improved from 0.49 rad to 0.17 rad on the most complex trajectory. The evaluation was conducted in controlled indoor conditions and offline mode, which defines the current scope of validation. Future work will extend the analysis to larger and more diverse environments and investigate extensions such as attention mechanisms, sensor fusion, and online learning to enhance adaptability in real-world deployment. Full article
(This article belongs to the Special Issue The Future of Robotics: AI Algorithms, Ethics, and Real-World Applications)
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