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Robotics
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AI-Powered Robotic Systems: Learning, Perception and Decision-Making
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AI-Powered Robotic Systems: Learning, Perception and Decision-Making

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 6642

Editors

Prof. Dr. Kensuke Harada

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Guest Editor
Graduate School of Engineering Science, Osaka University, Osaka 565-0871, Japan
Interests: grasping; manipulation; industrial robot; humanoid robot
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Genci Capi

E-Mail Website
Guest Editor
Human Assistive Robotics Lab, Department of Mechanical Engineering, Faculty of Science and Engineering, Hosei University, 3-7-2 Kajino-cho, Koganei-shi, Tokyo 184-8584, Japan
Interests: intelligent systems; BMI; AI robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. João Miguel da Costa Sousa

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Guest Editor
Department of Mechanical Engineering, IDMEC, Instituto Superior Tecnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Interests: computational intelligence and fuzzy systems; intelligent data analysis; smart industry; applications in energy and healthcare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence (AI) have dramatically transformed the capabilities of robotic systems, enabling them to perceive complex environments, learn from experience, and make autonomous decisions in real time. The integration of machine learning, deep learning, and probabilistic reasoning into robotics has led to substantial progress in various domains, including autonomous navigation, human–robot interaction, manipulation, and swarm coordination.

This Special Issue aims to bring together cutting-edge research and innovative applications of AI and robotics, with a particular focus on learning algorithms, perceptual systems, and decision-making frameworks.

Topics of interest include, but are not limited to, the following:

  • Learning-based control and planning for robotics;
  • Perception and sensor fusion using AI techniques;
  • Reinforcement learning and imitation learning in robotics;
  • Vision-based navigation and manipulation;
  • Real-time decision-making under uncertainty;
  • Cognitive robotics and adaptive behaviors;
  • Multi-agent coordination and decision-making;
  • AI applications in human–robot interaction;
  • Applications in service robotics, field robotics, healthcare, manufacturing, and more.

We welcome original research articles, comprehensive reviews, and case studies that address theoretical foundations, algorithm development, system implementations, and real-world deployments of AI-powered robotic systems. This Special Issue aims to provide a comprehensive overview of recent developments in the next generation of intelligent robots.

Prof. Dr. Kensuke Harada
Prof. Dr. Genci Capi
Prof. Dr. João Miguel da Costa Sousa
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-anonymized 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

  • learning-based control
  • planning for robotics
  • perception and sensor fusion
  • reinforcement learning
  • vision-based navigation
  • decision-making
  • cognitive robotics
  • adaptive behaviors
  • multi-agent
  • AI applications in human–robot interaction

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

Published Papers (6 papers)

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Research

37 pages, 2675 KB  
Article
Decentralized Shared Actor–Critic Learning for Collision-Aware Small-Team Multi-Robot Coverage
by Abzal E. Kyzyrkanov, Didar Yedilkhan, Saltanat Amirgaliyeva and Sergazy Narynov
Robotics 2026, 15(7), 119; https://doi.org/10.3390/robotics15070119 (registering DOI) - 25 Jun 2026
Abstract
This study presents a decentralized shared actor–critic framework for cooperative multi-robot coverage in continuous two-dimensional simulation. The method combines permutation-invariant local observations, continuous differential-drive control, and reward shaping based on stepwise Hungarian assignment distances, collision penalties, and time efficiency. Homogeneous teams of four, [...] Read more.
This study presents a decentralized shared actor–critic framework for cooperative multi-robot coverage in continuous two-dimensional simulation. The method combines permutation-invariant local observations, continuous differential-drive control, and reward shaping based on stepwise Hungarian assignment distances, collision penalties, and time efficiency. Homogeneous teams of four, five, and six agents are evaluated in an obstacle-free environment using five independent training seeds. In the final training window, the full reward configuration achieved full-team success rates of 98.2 ± 2.9% for four agents, 85.1 ± 18.0% for five agents, and 96.3 ± 2.0% for six agents, with mean landmark coverage above 96% in all cases. The lower mean in the five-agent setting was associated with higher seed-level variability dominated by one low-success seed. Reward ablations without assignment shaping or collision penalties remained viable, and seed-level tests did not show a statistically significant final-window advantage of the full reward configuration. The full configuration reached the 80% rolling-success threshold earlier in median terms, with the clearest seed-level support in the four-agent setting. Within-environment comparison showed higher full-team success than MADDPG and MAPPO under the matched training horizon and final-window protocol. Deterministic arena-size transfer from 15×15 to 30×30 showed decreasing full-team success as arena size increased, while partial landmark coverage remained higher than strict full-team completion. The results support the method for small homogeneous teams in the tested obstacle-free simulation, while larger teams, external obstacles, aerial-robot dynamics, formal safety guarantees, and hardware deployment remain future work. Full article
(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)
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28 pages, 7138 KB  
Article
Hybrid LLM-Genetic Programming: Supervising and Generating Diverse Behavior Trees for Autonomous Robot Evolution
by Chi Jie Tan, Eiji Hayashi, Abbe Mowshowitz and Way Soong Lim
Robotics 2026, 15(5), 98; https://doi.org/10.3390/robotics15050098 - 11 May 2026
Viewed by 701
Abstract
Genetic Programming (GP) for evolving Behavior Trees (BTs) in autonomous robots often suffer from premature convergence, even when adaptive mutation mechanisms are employed. This paper proposes a novel hybrid framework that integrates Large Language Model (LLM) supervision into GP, in which the LLM [...] Read more.
Genetic Programming (GP) for evolving Behavior Trees (BTs) in autonomous robots often suffer from premature convergence, even when adaptive mutation mechanisms are employed. This paper proposes a novel hybrid framework that integrates Large Language Model (LLM) supervision into GP, in which the LLM performs holistic population analysis, adaptively regulates mutation rates, and generates targeted BTs to proactively address behavioral gaps in the evolving population. Unlike conventional evolutionary operators, the LLM introduces high-level semantic guidance by seeding underrepresented behavioral archetypes, thereby complementing stochastic genetic variation with structured exploration. The proposed method is evaluated in a Unity-based multi-task robotic simulation environment. Experimental results show that the hybrid approach significantly outperforms baseline GP with standard adaptive mutation, achieving a 71.7% faster emergence of Complete Robots, a 65.2% faster emergence of Excellent Robots, and a 28% increase in behavioral diversity. Notably, the two systems exhibit opposite mutation dynamics: the LLM-guided system progressively reduces mutation rates to promote exploitation, whereas the baseline maintains a high mutation rate. In addition, the LLM generates approximately 40 targeted BTs per run, proactively seeding the population with underrepresented behavioral archetypes. These performance gains are obtained with only a 13% computational overhead. Full article
(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)
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25 pages, 4973 KB  
Article
LLM-Assisted Plan Execution for Robots in Dynamic Environments
by Juan Diego Peña-Narvaez, Rodrigo Pérez-Rodríguez, Juan Carlos Manzanares, Francisco Miguel Moreno and Francisco Martín-Rico
Robotics 2026, 15(4), 80; https://doi.org/10.3390/robotics15040080 - 15 Apr 2026
Viewed by 1006
Abstract
In recent years, planning frameworks have enabled the creation and execution of plans in robots using classical planning approaches based on the Planning Domain Definition Language (PDDL). The dynamic nature of the environments in which these robots operate requires that execution plans adapt [...] Read more.
In recent years, planning frameworks have enabled the creation and execution of plans in robots using classical planning approaches based on the Planning Domain Definition Language (PDDL). The dynamic nature of the environments in which these robots operate requires that execution plans adapt to new conditions, either by repairing plans to improve efficiency or because they are no longer valid. Determining the appropriate moment to initiate such repairs is the focus of our research. This paper presents a novel approach to this problem by using Large Language Models (LLMs) to make informed plan repair decisions during robot operation. Our approach introduces an LLM-based semantic evaluation heuristic that goes beyond the traditional heuristic methods employed in symbolic planning frameworks, while addressing the common hallucinations associated with task planning when relying solely on generative artificial intelligence. Our approach uses the semantic evaluation capabilities of LLMs to track environmental features and forecast hazards. This allows the system to proactively identify dangerous situations and adapt plans more efficiently. We experimentally demonstrate the validity of our approach using real robots in environments where both the environmental conditions and the goals to be achieved change dynamically. Full article
(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)
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18 pages, 9134 KB  
Article
An Autonomous Robotic System for Object Retrieval and Delivery: Enhancing Independence for Users Living with Disability and Older Adults
by Jincheng Li, Chenghao Lin, Amna Mazen and Youssef A. Bazzi
Robotics 2026, 15(2), 41; https://doi.org/10.3390/robotics15020041 - 12 Feb 2026
Viewed by 1216
Abstract
As the global population ages, there is a growing need for assistive technologies to help older adults maintain their independence. This work presents a cost-effective autonomous socially assistive robot designed for object retrieval and delivery, enhancing accessibility in home environments. The system is [...] Read more.
As the global population ages, there is a growing need for assistive technologies to help older adults maintain their independence. This work presents a cost-effective autonomous socially assistive robot designed for object retrieval and delivery, enhancing accessibility in home environments. The system is built on the Robot Operating System (ROS) framework and integrates three key components: the Pioneer P3-DX mobile robot for autonomous navigation, the ReactorX-200 robotic arm for pick-and-place operations, and the Kinect v2 RGB-D camera for object detection and localization. Users interact with the robot through natural language processing by issuing voice commands to retrieve various objects. Microsoft Azure-powered speech recognition processes these commands to extract keywords and then localize requested objects on a predefined building map. Pioneer P3-DX, equipped with a Hokuyo LiDAR, enables autonomous navigation and obstacle avoidance, while Kinect v2, integrated with the YOLOv8 algorithm, facilitates object recognition and localization. The robot retrieves and delivers the user’s requested objects while following the shortest available path. Experimental evaluations in a home environment demonstrate the system’s effectiveness in identifying and retrieving requested objects. The subsystems achieve a success rate of 85–95% across more than 50 runs, highlighting their strong performance. The proposed approach provides a proof of concept for future advancements in assistive robotics, demonstrating the seamless integration of advanced technologies into a cost-effective and user-friendly platform. Full article
(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)
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24 pages, 19000 KB  
Article
Scaling Functional Electrical Stimulation Control for Diverse Users Through Offline Distributional Reinforcement Learning
by Nat Wannawas, Jyotindra Narayan, Warakom Nerdnoi and Arsanchai Sukkuea
Robotics 2026, 15(2), 38; https://doi.org/10.3390/robotics15020038 - 8 Feb 2026
Viewed by 975
Abstract
Functional Electrical Stimulation (FES) can restore motor function; however, achieving precise multi-joint control remains challenging due to nonlinear muscle dynamics and fatigue. Reinforcement Learning (RL) offers a promising solution, but practical deployment is hindered by the need for patient-specific calibration. This study investigates [...] Read more.
Functional Electrical Stimulation (FES) can restore motor function; however, achieving precise multi-joint control remains challenging due to nonlinear muscle dynamics and fatigue. Reinforcement Learning (RL) offers a promising solution, but practical deployment is hindered by the need for patient-specific calibration. This study investigates offline RL approaches for controlling planar arm movements using heterogeneous datasets, aiming to enable zero-shot transfer to new users. We develop a biomechanical arm model in MuJoCo and evaluate four RL algorithms coupled with three offline techniques: conservative Q learning (SAC-CQL and QBR-CQL), Randomized Ensemble (QBR-REM), and distributional RL (IQNBR). Across all conditions, IQNBR demonstrates robust learning and superior control performance, achieving an average RMSE of 3.8±0.6 cm, even when trained on mixed-quality data. These results highlight the potential of distributional RL as a base learning method to build generic FES controllers that can operate without exhaustive calibration, with broader implications for controlling robots with human-like actuation systems. Full article
(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)
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25 pages, 1674 KB  
Article
Relaxed Monotonic QMIX (R-QMIX): A Regularized Value Factorization Approach to Decentralized Multi-Agent Reinforcement Learning
by Liam O’Brien and Hao Xu
Robotics 2026, 15(1), 28; https://doi.org/10.3390/robotics15010028 - 21 Jan 2026
Viewed by 1465
Abstract
Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, [...] Read more.
Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, which guarantees consistency with individual greedy policies but can severely limit expressiveness on tasks with non-monotonic agent interactions. This work revisits this design choice and proposes Relaxed Monotonic QMIX (R-QMIX), a simple regularized variant of QMIX that encourages but does not strictly enforce the monotonicity constraint. R-QMIX removes the sign constraints on the mixing network weights and introduces a differentiable penalty on negative partial derivatives of the joint value with respect to each agent’s utility. This preserves the computational benefits of value factorization while allowing the joint value to deviate from strict monotonicity when beneficial. R-QMIX is implemented in a standard PyMARL (an open-source MARL codebase) and evaluated on the StarCraft Multi-Agent Challenge (SMAC). On a simple map (3m), R-QMIX matches the asymptotic performance of QMIX while learning substantially faster. On more challenging maps (MMM2, 6h vs. 8z, and 27m vs. 30m), R-QMIX significantly improves both sample efficiency and final win rate (WR), for example increasing the final-quarter mean win rate from 42.3% to 97.1% on MMM2, from 0.0% to 57.5% on 6h vs. 8z, and from 58.0% to 96.6% on 27m vs. 30m. These results suggest that soft monotonicity regularization is a practical way to bridge the gap between strictly monotonic value factorization and fully unconstrained joint value functions. A further comparison against QTRAN (Q-value transformation), a more expressive value factorization method, shows that R-QMIX achieves higher and more reliably convergent win rates on the challenging SMAC maps considered. Full article
(This article belongs to the Special Issue AI-Powered Robotic Systems: Learning, Perception and Decision-Making)
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