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Robotics
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Applications of Neural Networks in Robot Control
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Applications of Neural Networks in Robot Control

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "AI in Robotics".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 4017

Special Issue Editors

Prof. Dr. Luca Patanè

E-Mail Website
Guest Editor
Department of Engineering, University of Messina, 98166 Messina, Italy
Interests: nonlinear system modeling and control; bio-robotics; locomotion control; spiking neural networks; insect-inspired control systems; system identification and soft sensor development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paolo Arena

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettric Elettronica e Informatica, University of Catania, 95125 Catania, Italy
Interests: nonlinear system modeling and control; bio-inspired robots; adaptive locomotion; learning systems; insect brain architectures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of robotics has made significant strides in recent years. Robots are increasingly becoming an integral part of various industries and daily life. This progress has largely been driven by advances in artificial intelligence, including neural-network-based applications. Neural networks have enhanced the capabilities of robots, enabling them to perform complex tasks, adapt to dynamic environments, and more effectively interact with humans. As a result, the integration between neural networks and robot control has become a key research area with great potential for innovation and impact. However, many aspects still need to be further explored: the computational effort needed for real-time vision-based control applications, the need for custom hardware implementations for specific tasks, the use of continuous learning for efficient adaptation to dynamically changing environments, the problem of dynamic stability when dealing with multi-link robots, and many others.

Last but not least, in addition to AI-based neural structures, neuromorphic spiking neural networks have recently been attracting attention because of their similarity to living neural tissue, but also because of their impressive dynamic behaviour, which would represent a potential alternative approach for robot control.

The proposed Special Issue aims to address these and other relevant research topics, as well as to consolidate and present the latest developments in the application of neural networks to robot control in different domains: from traditional wheeled robots to legged robots and from high-level motion planning tasks to single limb movements. Relevant potential applications range from the exploration of unstructured terrain to industrial automation and healthcare.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Deep learning for robot perception;
  • Reinforcement learning for robot control in dynamic environments;
  • Neural-network-based motion planning and control;
  • Human–robot interaction with deep learning techniques;
  • Autonomous systems and navigation;
  • Spiking neural networks for robot control;
  • Bio-inspired brain models;
  • Neuromorphic systems;
  • Neural networks and soft robotics.

We look forward to receiving your contributions.

Prof. Dr. Luca Patanè
Prof. Dr. Paolo Arena
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. Robotics 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

  • neural control
  • spiking neural networks
  • bio-inspired brain models
  • neuromorphic systems
  • learning in bio-inspired robots
  • sensory–motor coordination
  • soft robotics
  • legged robotics

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Published Papers (3 papers)

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Research

16 pages, 22837 KiB  
Article
Learning to Walk with Adaptive Feet
by Antonello Scaldaferri, Franco Angelini and Manolo Garabini
Robotics 2024, 13(8), 113; https://doi.org/10.3390/robotics13080113 - 24 Jul 2024
Viewed by 550
Abstract
In recent years, tasks regarding autonomous mobility favoredthe use of legged robots rather than wheeled ones thanks to their higher mobility on rough and uneven terrains. This comes at the cost of more complex motion planners and controllers to ensure robot stability and [...] Read more.
In recent years, tasks regarding autonomous mobility favoredthe use of legged robots rather than wheeled ones thanks to their higher mobility on rough and uneven terrains. This comes at the cost of more complex motion planners and controllers to ensure robot stability and balance. However, in the case of quadrupedal robots, balancing is simpler than it is for bipeds thanks to their larger support polygons. Until a few years ago, most scientists and engineers addressed the quadrupedal locomotion problem with model-based approaches, which require a great deal of modeling expertise. A new trend is the use of data-driven methods, which seem to be quite promising and have shown great results. These methods do not require any modeling effort, but they suffer from computational limitations dictated by the hardware resources used. However, only the design phase of these algorithms requires large computing resources (controller training); their execution in the operational phase (deployment), takes place in real time on common processors. Moreover, adaptive feet capable of sensing terrain profile information have been designed and have shown great performance. Still, no dynamic locomotion control method has been specifically designed to leverage the advantages and supplementary information provided by this type of adaptive feet. In this work, we investigate the use and evaluate the performance of different end-to-end control policies trained via reinforcement learning algorithms specifically designed and trained to work on quadrupedal robots equipped with passive adaptive feet for their dynamic locomotion control over a diverse set of terrains. We examine how the addition of the haptic perception of the terrain affects the locomotion performance. Full article
(This article belongs to the Special Issue Applications of Neural Networks in Robot Control)
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19 pages, 1787 KiB  
Article
Learning Advanced Locomotion for Quadrupedal Robots: A Distributed Multi-Agent Reinforcement Learning Framework with Riemannian Motion Policies
by Yuliu Wang, Ryusuke Sagawa and Yusuke Yoshiyasu
Robotics 2024, 13(6), 86; https://doi.org/10.3390/robotics13060086 - 28 May 2024
Viewed by 1112
Abstract
Recent advancements in quadrupedal robotics have explored the motor potential of these machines beyond simple walking, enabling highly dynamic skills such as jumping, backflips, and even bipedal locomotion. While reinforcement learning has demonstrated excellent performance in this domain, it often relies on complex [...] Read more.
Recent advancements in quadrupedal robotics have explored the motor potential of these machines beyond simple walking, enabling highly dynamic skills such as jumping, backflips, and even bipedal locomotion. While reinforcement learning has demonstrated excellent performance in this domain, it often relies on complex reward function tuning and prolonged training times, and the interpretability is not satisfactory. Riemannian motion policies, a reactive control method, excel in handling highly dynamic systems but are generally limited to fully actuated systems, making their application to underactuated quadrupedal robots challenging. To address these limitations, we propose a novel framework that treats each leg of a quadrupedal robot as an intelligent agent and employs multi-agent reinforcement learning to coordinate the motion of all four legs. This decomposition satisfies the conditions for utilizing Riemannian motion policies and eliminates the need for complex reward functions, simplifying the learning process for high-level motion modalities. Our simulation experiments demonstrate that the proposed method enables quadrupedal robots to learn stable locomotion using three, two, or even a single leg, offering advantages in training speed, success rate, and stability compared to traditional approaches, and better interpretability. This research explores the possibility of developing more efficient and adaptable control policies for quadrupedal robots. Full article
(This article belongs to the Special Issue Applications of Neural Networks in Robot Control)
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16 pages, 11050 KiB  
Article
A Control Interface for Autonomous Positioning of Magnetically Actuated Spheres Using an Artificial Neural Network
by Victor Huynh, Basam Mutawak, Minh Quan Do, Elizabeth A. Ankrah, Pouya Kassaeiyan, Irving N. Weinberg, Nathalia Peixoto, Qi Wei and Lamar O. Mair
Robotics 2024, 13(3), 39; https://doi.org/10.3390/robotics13030039 - 28 Feb 2024
Viewed by 1566
Abstract
Electromagnet arrays show significant potential in the untethered guidance of particles, devices, and eventually robots. However, complications in obtaining accurate models of electromagnetic fields pose challenges for precision control. Manipulation often requires the reduced-order modeling of physical systems, which may be computationally complex [...] Read more.
Electromagnet arrays show significant potential in the untethered guidance of particles, devices, and eventually robots. However, complications in obtaining accurate models of electromagnetic fields pose challenges for precision control. Manipulation often requires the reduced-order modeling of physical systems, which may be computationally complex and may still not account for all possible system dynamics. Additionally, control schemes capable of being applied to electromagnet arrays of any configuration may significantly expand the usefulness of any control approach. In this study, we developed a data-driven approach to the magnetic control of a neodymium magnets (NdFeB magnetic sphere) using a simple, highly constrained magnetic actuation architecture. We developed and compared two regression-based schemes for controlling the NdFeB sphere in the workspace of a four-coil array of electromagnets. We obtained averaged submillimeter positional control (0.85 mm) of a NdFeB hard magnetic sphere in a 2D plane using a controller trained using a single-layer, five-input regression neural network with a single hidden layer. Full article
(This article belongs to the Special Issue Applications of Neural Networks in Robot Control)
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