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Actuators
Special Issues
Machine Learning for Actuation and Control in Robotic Joint Systems
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Machine Learning for Actuation and Control in Robotic Joint Systems

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Robotics".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 778

Special Issue Editors

Dr. Gao Huang

E-Mail Website
Guest Editor
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Interests: robot joint design; motion control; machine learning
Dr. Pan Yu

E-Mail Website
Guest Editor
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Interests: robot control; multi-agent cooperative control; high-precision control of electromechanical systems; active disturbance rejection control; advanced robust control; control theory and application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robotic joint systems are fundamental to modern robotics, enabling precise motion, adaptability, and autonomy in applications ranging from industrial automation to biomedical devices. Recent advances in machine learning (ML) have opened new possibilities for enhancing the actuation and control of these systems, improving efficiency, robustness, and adaptability in dynamic environments. This Special Issue seeks to explore cutting-edge research at the intersection of ML, actuation technologies, and control strategies for robotic joints.

We aim to showcase innovative methodologies, experimental validations, and reviews that address challenges and opportunities in ML-driven robotic joint systems. Researchers are encouraged to submit original work that bridges the gap between theoretical ML advancements and practical robotic applications.

Dr. Gao Huang
Dr. Pan Yu
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. Actuators 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 2400 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

  • robotic joint control
  • robot joint design
  • machine learning
  • modeling, control, and optimization of electromechanical systems
  • adaptive control
  • neural networks
  • soft robot and actuation
  • humanoid robot
  • bio-inspired robot design
  • optimization of robot transmission
  • machine learning-based motion control

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Published Papers (1 paper)

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Research

20 pages, 6648 KB  
Article
Sensorless Collision Detection and Classification in Collaborative Robots Using Stacked GRU Networks
by Jong Hyeok Lee, Minjae Hong and Kyu Min Park
Actuators 2026, 15(4), 206; https://doi.org/10.3390/act15040206 - 4 Apr 2026
Viewed by 325
Abstract
The increasing deployment of collaborative robots in industrial manufacturing environments has enabled close human–robot collaboration, making rapid and reliable collision detection essential for worker safety. This paper presents a learning-based framework for real-time detection and classification of hard and soft collisions using stacked [...] Read more.
The increasing deployment of collaborative robots in industrial manufacturing environments has enabled close human–robot collaboration, making rapid and reliable collision detection essential for worker safety. This paper presents a learning-based framework for real-time detection and classification of hard and soft collisions using stacked Gated Recurrent Unit (GRU) networks. A two-stage pipeline is introduced, in which collision detection and collision type classification are performed sequentially using separate models, and its performance is validated through extensive experiments on a collision dataset collected from a six-joint collaborative robot executing random point-to-point motions. Without requiring joint torque sensors, unmodeled joint friction is implicitly compensated through learning for both detection and classification. Compared to our previous work, the proposed method achieves improved detection performance, and its robustness is further demonstrated through systematic generalization experiments under simulated dynamic model uncertainties. In addition, the classification model accurately distinguishes between hard and soft collisions, providing a basis for differentiated post-collision reaction strategies. Overall, the proposed sensorless collision detection and classification framework provides a practical and cost-effective solution for real-world industrial human–robot collaboration. Full article
(This article belongs to the Special Issue Machine Learning for Actuation and Control in Robotic Joint Systems)
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