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Technologies
Special Issues
Collaborative Robotics and Human-AI Interactions
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Collaborative Robotics and Human-AI Interactions

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1324

Special Issue Editors

Prof. Dr. Damith Herath

E-Mail Website
Guest Editor
Collaborative Robotics Laboratory, University of Canberra, Canberra, Australia
Interests: robotic art; human–robot interactions; social robots; simultaneous localization and mapping
Dr. Janie Busby-Grant

E-Mail Website
Guest Editor
Centre for Applied Psychology, University of Canberra, Canberra, ACT, Australia
Interests: psychology; human–robot interactions
Dr. Xing Wang

E-Mail Website
Guest Editor
Robotics and Autonomous Systems Group, Data61, CSIRO, Brisbane, Australia
Interests: soft robotics; computational design; agricultural robots; sensors and actuators

Special Issue Information

Dear Colleagues,

We cordially invite you to contribute to this Special Issue of Technologies MDPI (JCR Q1) with the dedicated topic of ‘Collaborative Robotics and Human–AI Interactions’, a field that is rapidly reshaping the way humans and machines work and evolve together. The boundaries between human capability and machine intelligence are increasingly blurred, as robots and AI systems shift from isolated tools to collaborative partners capable of shared decision-making, adaptive behaviour, and real-time responsiveness in dynamic environments.

Today, it is no longer sufficient for machines to simply follow commands; they must learn, adapt, and cooperate with humans in safe, intuitive, and meaningful ways. Collaborative robots are already transforming industries, healthcare, education, agriculture, and everyday life by working alongside people rather than replacing them. These systems are not just mechanical extensions of us, but active agents in a new, shared intelligence, where creativity, ethics, and trust are central. As we stand at the crossroads of technology and humanity, we must embrace this opportunity to design systems that are not only efficient, but that are also empathetic.

This Special Issue seeks to bring together cutting-edge research and interdisciplinary approaches that reflect the spirit of intelligent robotics, including sensors and actuators, and collaboration between humans and intelligent systems. We welcome submissions including, but not limited to, the following areas:

  1. Collaborative robots;
  2. Human–robot interactions;
  3. Artificial intelligence;
  4. Sensors and actuators;
  5. Agricultural robots;
  6. Service and care robots;
  7. Mechanical and mechatronic systems;
  8. Trust, cybersecurity, and safety in collaborative robotics.

Join us in shaping a future where human potential is empowered—not replaced—by intelligent machines.

Prof. Dr. Damith Herath
Dr. Janie Busby-Grant
Dr. Xing Wang
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. Technologies 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 1600 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

  • robotics
  • artificial intelligence
  • computer vision
  • sensors and actuators
  • human–robot interactions
  • cobots

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

Published Papers (3 papers)

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Research

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25 pages, 429 KB  
Article
CALM: Continual Associative Learning Model via Sparse Distributed Memory
by Andrey Nechesov and Janne Ruponen
Technologies 2025, 13(12), 587; https://doi.org/10.3390/technologies13120587 - 13 Dec 2025
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Abstract
Sparse Distributed Memory (SDM) provides a biologically inspired mechanism for associative and online learning. Transformer architectures, despite exceptional inference performance, remain static and vulnerable to catastrophic forgetting. This work introduces Continual Associative Learning Model (CALM), a conceptual framework that defines the theoretical base [...] Read more.
Sparse Distributed Memory (SDM) provides a biologically inspired mechanism for associative and online learning. Transformer architectures, despite exceptional inference performance, remain static and vulnerable to catastrophic forgetting. This work introduces Continual Associative Learning Model (CALM), a conceptual framework that defines the theoretical base and integration logic for the cognitive model seeking to establish continual, lifelong adaptation without retraining by combining SDM system with lightweight dual-transformer modules. The architecture proposes an always-online associative memory for episodic storage (System 1), as well as a pair of asynchronous transformer consolidate experience in the background for uninterrupted reasoning and gradual model evolution (System 2). The framework remains compatible with standard transformer benchmarks, establishing a shared evaluation basis for both reasoning accuracy and continual learning stability. Preliminary experiments using the SDMPreMark benchmark evaluate algorithmic behavior across multiple synthetic sets, confirming a critical radius-threshold phenomenon in SDM recall. These results represent deterministic characterization of SDM dynamics in the component level, preceding the integration in the model level with transformer-based semantic tasks. The CALM framework provides a reproducible foundation for studying continual memory and associative learning in hybrid transformer architectures, although future work should involve experiments with non-synthetic, high-load data to confirm scalable behavior in high interference. Full article
(This article belongs to the Special Issue Collaborative Robotics and Human-AI Interactions)
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34 pages, 3473 KB  
Article
Workspace Definition in Parallelogram Manipulators: A Theoretical Framework Based on Boundary Functions
by Luis F. Luque-Vega, Jorge A. Lizarraga, Dulce M. Navarro, Jose R. Navarro, Rocío Carrasco-Navarro, Emmanuel Lopez-Neri, Jesús Antonio Nava-Pintor, Fabián García-Vázquez and Héctor A. Guerrero-Osuna
Technologies 2025, 13(9), 404; https://doi.org/10.3390/technologies13090404 - 5 Sep 2025
Viewed by 832
Abstract
Robots with parallelogram mechanisms are widely employed in industrial applications due to their mechanical rigidity and precise motion control. However, the analytical definition of feasible workspace regions free from self-collisions remains an open challenge, especially considering the nonlinear and composite nature of such [...] Read more.
Robots with parallelogram mechanisms are widely employed in industrial applications due to their mechanical rigidity and precise motion control. However, the analytical definition of feasible workspace regions free from self-collisions remains an open challenge, especially considering the nonlinear and composite nature of such regions. This work introduces a mathematical model grounded in a collision theorem that formalizes boundary functions based on joint variables and geometric constraints. These functions explicitly define the envelope of safe configurations by evaluating relative positions between critical structural components. Using the MinervaBotV3 as a case study, the symbolic joint-space boundaries and their corresponding geometric regions in both 2D and 3D are computed and visualized. The feasible region is refined through centroid-based scaling to introduce safety margins and avoid singularities. The results show that this framework enables analytically continuous workspace representations, improving trajectory planning and reliability in constrained environments. Future work will extend this method to spatial mechanisms and real-time implementations in hybrid robotic systems. Full article
(This article belongs to the Special Issue Collaborative Robotics and Human-AI Interactions)
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Review

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59 pages, 7547 KB  
Review
Turn-Taking Modelling in Conversational Systems: A Review of Recent Advances
by Rutherford Agbeshi Patamia, Ha Pham Thien Dinh, Ming Liu and Akansel Cosgun
Technologies 2025, 13(12), 591; https://doi.org/10.3390/technologies13120591 - 15 Dec 2025
Abstract
Effective turn-taking is fundamental to conversational interactions, shaping the fluidity of communication across human dialogues and interactions with spoken dialogue systems (SDS). Despite its apparent simplicity, conversational turn-taking involves complex timing mechanisms influenced by various linguistic, prosodic, and multimodal cues. This review synthesises [...] Read more.
Effective turn-taking is fundamental to conversational interactions, shaping the fluidity of communication across human dialogues and interactions with spoken dialogue systems (SDS). Despite its apparent simplicity, conversational turn-taking involves complex timing mechanisms influenced by various linguistic, prosodic, and multimodal cues. This review synthesises recent theoretical insights and practical advancements in understanding and modelling conversational timing dynamics, emphasising critical phenomena such as voice activity (VA), turn floor offsets (TFO), and predictive turn-taking. We first discuss foundational concepts, such as voice activity detection (VAD) and inter-pausal units (IPUs), and highlight their significance for systematically representing dialogue states. Central to the challenge of interactive systems is distinguishing moments when conversational roles shift versus when they remain with the current speaker, encapsulated by the concepts of “hold” and “shift”. The timing of these transitions, measured through Turn Floor Offsets (TFOs), aligns closely with minimal human reaction times, suggesting biological underpinnings while exhibiting cross-linguistic variability. This review further explores computational turn-taking heuristics and models, noting that simplistic strategies may reduce interruptions yet risk introducing unnatural delays. Integrating multimodal signals, prosodic, verbal, visual, and predictive mechanisms is emphasised as essential for future developments in achieving human-like conversational responsiveness. Full article
(This article belongs to the Special Issue Collaborative Robotics and Human-AI Interactions)
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