Research and Education in Robotics: A Comprehensive Review, Trends, Challenges, and Future Directions

Abstract

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers.

1. Introduction

Robotics constitutes a multidisciplinary domain that synthesizes principles from engineering, computer science, artificial intelligence (AI), and cognitive science to design, develop, and deploy intelligent systems known as robots. These systems operate under two primary paradigms: teleoperation, where human oversight guides functionality, and autonomous operation, which leverages advanced algorithms and sensory feedback for independent decision-making [1,2]. This field has emerged as a transformative force across diverse sectors, addressing challenges in industrial automation, healthcare, education, and exploration by undertaking tasks that are repetitive, hazardous, or cognitively demanding for humans [3]. Beyond mere machine creation, robotics encompasses the integration of interconnected devices and adaptive systems that collaborate with humans to augment capabilities, optimize efficiency, and pioneer innovative solutions [4]. As an interdisciplinary endeavor, robotics bridges theoretical frameworks and applied methodologies, fostering synergistic convergence between technological innovation and domain-specific implementations. This integration advances operational paradigms and redefines the frontiers of human–machine collaboration through adaptive socio-technical ecosystems [5]. Recent advancements in cognitive intelligence have enabled machines to act as peer collaborators, leveraging autonomous decision-making, reasoning, and situational awareness to augment human capabilities.

Robots are deployed across a broad spectrum of domains and demonstrate exceptional versatility and adaptability in structured and unstructured environments. In the industrial contexts, they play a critical role in automating manufacturing processes and enhancing precision, efficiency, and safety. Their integration into fields such as deep-sea exploration and space missions has enabled access to environments that are otherwise hazardous or inaccessible to humans [6]. Beyond technical applications, robots are increasingly utilized in behavioral sciences to study human and animal interactions and in healthcare to support individuals with disabilities through assistive technologies [7]. Moreover, robotic systems actively shape domains such as logistics, autonomous delivery, and defense operations. In addition to utilitarian functions, robots have been engineered for entertainment and leisure, enriching user engagement through interactive and socially responsive designs.

An effective robotic performance across diverse applications requires advanced programming architectures and highly precise control systems. As a fundamentally interdisciplinary field, robotics synthesizes concepts from mechanical, electrical, and electronic engineering, alongside computer, cognitive, and biomedical sciences [4]. Central to this integration is the field of mechatronics, which combines mechanical engineering, electronics, computer control, and system design. Mechatronics has become instrumental in driving innovations in adaptive and intelligent robotic systems, enabling responsive and autonomous behaviors [8]. The overarching objective of robotics research is to develop systems capable of operating autonomously or collaboratively with humans, dynamically adapting to complex environments, and executing specialized tasks efficiently and reliably [9].

Robotics research has become a foundational pillar of contemporary technological innovation propelled by dynamic, interdisciplinary collaboration across a wide array of scientific and engineering disciplines. The field is witnessing rapid convergence between artificial intelligence, materials science, and biomechatronics, an interplay that is accelerating advancements from nanoscale medical robots to autonomous planetary exploration systems. Recent breakthroughs in neuromorphic computing have enabled robotic systems to process sensory information with a level of efficiency and biological fidelity that mirrors the neural architectures in nature [10]. Simultaneously, progress in soft robotics is reshaping the landscape of human–machine interaction, offering safer, more adaptable, and context-aware interfaces [11]. These developments underscore the centrality of cutting-edge research in expanding the boundaries of the perception, learning, and accomplishment of robotic systems.

The escalating global demand for skilled roboticists has led to a surge in educational initiatives aimed at cultivating expertise in this rapidly evolving field. As robotics has become increasingly integrated into industries such as healthcare, manufacturing, agriculture, and autonomous systems, the need for professionals with interdisciplinary knowledge has never become more critical [12]. Robotics education spans from early STEM outreach to advanced graduate research, building skills in mechanical design, embedded systems, AI, and human–robot interaction. The demand for roboticists has catalyzed global educational initiatives by blending project-based learning with cloud-enabled simulation platforms [13]. Modern curricula emphasize three competency pillars: computational thinking through visual programming interfaces [14], mechatronic system integration using modular kits [15], and cross-disciplinary collaboration through international robot competitions [16]. Leading institutions worldwide are reshaping curricula to reflect the convergence of disciplines within robotics, emphasizing not only technical proficiency but also ethical, social, and collaborative dimensions [17]. This educational momentum is essential to sustaining innovation, ensuring responsible deployment, and preparing the next generation of researchers, engineers, and educators who will define the future of robotics.

Robotics: Global impact and market trends

The global robotics sector is undergoing rapid expansion, driven by technological convergence and increasing demand across industries.

Table 1. Global impact of robotics on business and employment (Sources: International Federation of Robotics (IFR) [18], The World Economic Forum (WEF) [19], Statista [20], MIT Sloan [21]).

Category	Key Statistics
Global Market Size	Robotics market projected to reach USD 165 Billion by 2028
Educational Robot Market Size	Valued at USD 1.37 Billion in 2024 and is estimated to grow at a CAGR of 28.8% from 2025 to 2030
Industrial Robot Deployment	4.28 million operational robots worldwide (2023)
Major Markets	China, Japan, US, Republic of Korea, and Germany (account for 74% of global robot installations)
Service Robotics	Expected to generate USD 40.6 Billion in revenue by 2025
Economic Impact	Automation expected to displace 85M jobs and create 97 Million new roles by 2025 (WEF)
Wage Impact	Each robot added per 1000 workers decreases wages by 0.42% in the US
Future Projection	By 2030, 80% of humans expected to interact with robots daily

summarizes the key statistics that highlight the scale and diversity of this transformation. The total robotics market is projected to surpass USD 165 billion by 2028, underscoring the sector’s commercial viability. A particularly striking trend is the explosive growth in educational robotics, which is expected to grow at a CAGR of 28.8%, reflecting the surging demand for robotics competencies and STEM education.

The deployment of over 4.28 million industrial robots globally, primarily concentrated in China, Japan, the US, Republic of Korea, and Germany, reveals a geographic imbalance in robotics adoption. These countries dominate 74% of the global installations, highlighting both opportunities and disparities in technological access. Service robotics, an emerging category in logistics, healthcare, and consumer markets, is forecast to generate USD 40.6 billion by 2025, signaling diversification beyond manufacturing.

Societal impact is also profound. According to the World Economic Forum, automation is projected to displace 85 million jobs and create 97 million new roles by 2025. While this suggests a net positive effect on employment, disruption necessitates urgent educational reform to retrain and reskill the workforce. Furthermore, wage suppression data, showing a 0.42% decline per robot added per 1000 workers, raises equity concerns that demand policy attention. By 2030, it is estimated that 80% of the population will interact with robots daily, illustrating not only economic but also cultural and behavioral shifts in human–robot coexistence.

Collectively, these data underscore the urgency for educational systems, particularly at the university level, to evolve in tandem with the robotics industry, equipping learners not only with technical know-how, but also adaptability, ethical foresight, and interdisciplinary fluency.

This rapid development necessitates a new generation of roboticists equipped with both practical skills and theoretical knowledge to effectively design, develop, and deploy intelligent robotic systems. As robots increasingly transition from controlled laboratory settings to complex, real-world environments, the demands for research innovation and educational frameworks continue to intensify.

This article explores robotics through both technological and educational lenses, with the aim of supporting researchers, educators, and policymakers in understanding current developments and anticipating future needs. It integrates historical analysis, technological trends, instructional strategies, and global initiatives to present a unified perspective on the evolving robotic landscape.

• Main Contributions

This review distinguishes itself from the existing literature by offering a holistic and integrative analysis of robotics from both technological and educational perspectives. Specifically, the article makes the following novel contributions:

• It presents a multi-dimensional taxonomy of robotics technologies encompassing AI, soft robotics, quantum robotics, IoRT, and neuromorphic computing, framed through recent advancements and real-world implementations.
• It systematically connects these technological developments to evolving educational practices at all levels, from K–12 to postgraduate research, highlighting instructional innovations such as digital twins, remote laboratories, and AI-driven tutoring.
• It offers a comparative analysis of educational tools, platforms, and curricula supported by detailed tables and figures.
• It outlines forward-looking, feasible future directions that incorporate technological, ethical, sustainable, and inclusive principles across robotics research and pedagogy.
• It introduces a curriculum-level case study from the German Jordanian University (GJU), showcasing a structured robotics integration across undergraduate coursework, project-based learning, capstone design, and competitive activities. This example demonstrates a scalable, practice-driven educational model that bridges theory and application within an industry-aligned academic framework.

This dual focus provides an actionable roadmap for researchers, educators, students and policymakers, filling a critical gap in the robotics literature.

2. Review Methodology

To ensure scholarly rigor and replicability, this review followed a structured methodology for literature selection and synthesis. This process was conducted in three stages: (1) database search and literature identification, (2) screening and inclusion, and (3) thematic categorization and synthesis.

2.1. Literature Search Strategy

A comprehensive literature search was performed using major academic databases including IEEE Xplore, Scopus, Web of Science, SpringerLink, ACM Digital Library, and Google Scholar. The search was restricted to peer-reviewed journal articles, conference proceedings, and authoritative reports published between 2010 and 2025, with a particular focus on publications from the last 5 years to capture recent advances.

2.2. Search Terms and Keywords

The search queries combined the keywords relevant to robotics research and education. The sample search strings included the following:

• “robotics research” AND “technological trends”
• “robotics education” OR “educational robotics” AND “STEM learning”
• “robot manipulation” AND “grasping techniques”
• “human-robot interaction” OR “collaborative robotics”
• “robotics curriculum” AND “university education”

Boolean operators and truncation were used to expand the scope and improve relevance.

2.3. Inclusion and Exclusion Criteria

Articles were included based on the following criteria:

• Focused on robotics technologies, education, applications, or pedagogical tools.
• Peer-reviewed publications or reputable white papers.
• Published in English between 2010 and 2025.
• Relevance to core themes, such as HRI, perception, motion planning, curriculum design, and educational tools.

Exclusion criteria included:

• Duplicate records or inaccessible full texts.
• Studies not directly related to robotics or educational implementations.
• Publications in non-peer-reviewed outlets lacking technical or empirical contributions.

2.4. Thematic Organization

The final selection of 121 sources was categorized into thematic clusters representing key areas of interest: robotics research domains (e.g., manipulation, sensing, and motion planning), robotics applications, and robotics in education (e.g., K–12, undergraduate, and graduate programs). This categorization informed the structure of the review and allowed for integrative synthesis from technological and pedagogical perspectives.

3. Robotics

3.1. A Journey Through Time: The Evolution of Robotics

The concept of creating machines that mimic human actions has fascinated humanity for several centuries. While the term “robot” was coined relatively recently, the seeds of robotics were sown long ago, with ancient civilizations dreaming of automata and self-operating devices.

3.1.1. The Dawn of Automata: Early Civilizations and the Greek Legacy [22]

The earliest examples of automata can be traced to ancient Egypt and Greece. With their rich tradition of philosophy and mechanics, Greeks envisioned complex machines that could perform tasks autonomously. Hero of Alexandria, a renowned mathematician and engineer, designed intricate devices powered by water, steam, and air pressure. His automata, including self-filling vessels and moving figures, showcased the early inventors’ ingenuity.

3.1.2. The Golden Age of Islamic Civilization: Al-Jazari, the Father of Robotics [23,24]

While the concept of automata continued to intrigue scholars throughout the Middle Ages, it was during the Islamic Golden Age that the field truly flourished. Ismail al-Jazari, a brilliant polymath from 13th-century Mesopotamia, is often hailed as the “father of robotics” [25]. His magnum opus, “The Book of Knowledge of Ingenious Mechanical Devices,“ detailed a vast array of automata, including musical automata, humanoid robots, and intricate clockwork mechanisms. Al-Jazari’s innovations, such as the use of cams and cranks to control movements, laid the foundation for many principles that underpin modern robotics.

3.1.3. The Renaissance and Industrial Revolution: Mechanical Marvels and Rise of Automation [26]

The Renaissance witnessed renewed interest in automata, with figures such as Leonardo da Vinci designing intricate mechanical knights and other human-like machines. However, the Industrial Revolution has transformed the landscape of automation. The development of steam engines and other power sources has fueled the development of increasingly complex machines, from automated looms to early factory robots. These machines, which do not resemble humans, have marked a significant step towards the automation of labor-intensive tasks.

3.1.4. The 20th Century: The Birth of Modern Robotics [27]

The 20th century saw a remarkable acceleration in the development of robotics. The term “robot” itself was coined by Czech playwright Karel Capek in his 1920 play Rossum’s Universal Robots. The 20th century marked the advent of modern robotics, with significant milestones shaping automated industries. In 1954, George Devol patented the first programmable robot in 1961, which led to the creation of Unimate, the world’s first industrial robot. Unimate was initially deployed in General Motors’ assembly lines to handle tasks such as die casting, welding, and revolutionizing manufacturing processes. This era also witnessed the development of the Stanford Arm in 1969, an all-electric, 6-axis articulated robot designed by Victor Scheinman, which expanded the applications of robotics to more sophisticated tasks such as assembly and welding. These innovations have laid the groundwork for the complex automation systems prevalent in modern industries.

3.1.5. The Digital Age: [3,28,29]

The rapid advancement of modern robotics has been propelled by digital computing and is largely attributed to the seamless convergence of advanced information and communication technologies (ICT), embedded systems, high-performance sensors and actuators, AI, 3D printing, smart materials, real-time cloud robotics, IoT, and progress in control theory. Real-time cloud robotics and the IoT have revolutionized robotic capabilities by enabling interconnected systems to share computational power and data instantaneously, thereby enhancing scalability and collaboration. Smart materials, such as self-healing polymers and shape-memory alloys, add flexibility and adaptability, whereas the control theory ensures stability and precision in operation. Embedded systems and AI facilitate autonomy, real-time decision-making, and learning, and 3D printing accelerates the development of highly customized designs. These technologies collectively underpin the transformative potential of modern robotics in smart manufacturing, autonomous vehicles, healthcare, and swarm robotics.

3.2. A Taxonomy of Robotics

Robotics, an interdisciplinary domain that integrates engineering, computer science, and applied mathematics, encompasses a diverse array of systems and applications. These systems can be systematically classified through a structured taxonomy based on key attributes, such as functional purpose, autonomy level, morphological characteristics, and operational environment [30]. A clear taxonomic framework simplifies the complexity of the field and aligns robotic systems with their technologies and applications. This classification serves as a foundational tool for advancing research, design, and interdisciplinary collaboration. A comprehensive classification matrix delineating these categories accompanied by representative robotic archetypes is presented in

Table 2. Classification of robots based on their types and functionalities.

Class of Robots	Types	Examples
Industrial Robots	Articulated Robots SCARA Robots Cartesian Robots (Gantry) Cylindrical Robots Delta Robots (Parallel) Polar Robots (Spherical) Collaborative Robots (Cobots)
Autonomous Mobile Robots	Wheeled Robots (UGV) Legged Robots Tracked Robots Underwater Robots (ROVs/AUVs) Hybrid Robots Autonomous Vehicles
Flying Robots, Drones, UAVs	Wing-Type Flapping-wing UAVs Fixed-wing UAVs Blimps, Gliders, VTOL Rotor-Type (Drones) Cyclo-copters Tri/Quad-copters Hexa/Octa-copters
Biologically Inspired	Humanoids Soft Robots Snake Robots Climbing Robots
Service Robots	Household Robots Healthcare Robots Education/Entertainment Security/Surveillance Delivery Robots

.

3.2.1. By Functionality (Application) [18,31]

Robotic systems can be categorized into two primary domains based on their functional applications: industrial and service. Industrial robots, as defined by the International Federation of Robotics (IFR), are programmable automation platforms engineered for manufacturing processes, such as precision assembly, welding, and material handling, adhering to globally standardized operational protocols. Conversely, service robots are designed for non-industrial operational parameters, fulfilling roles in healthcare (surgical assistance and rehabilitation), logistics (warehouse automation), and personal care.

3.2.2. By Level of Autonomy [32]

Robotic systems encompass a continuum of autonomy, ranging from manually operated platforms that require full human control to fully autonomous agents that are capable of self-directed task execution in dynamic environments. At the lowest autonomy tier, manual systems (e.g., teleoperated industrial arms) depend entirely on human operators for guidance. At the highest tier, fully autonomous robots leverage advanced perception, decision-making frameworks, and adaptive control architectures to operate independently in unstructured settings. This continuum of autonomy is often formalized through frameworks such as the level of robot autonomy (LORA) [32,33].

3.2.3. By Physical Configuration [34,35]

Robotic systems can be taxonomically categorized on the basis of their morphological design, which defines their physical architecture, mobility mechanisms, and environmental interaction capabilities. Morphology encompasses structural attributes such as kinematic chain topology (e.g., serial vs. parallel linkages), joint configuration (revolute, prismatic, or spherical), actuator characteristics (e.g., torque density, power-to-weight ratios), material composition, and sensor distribution [36], and directly governs a robot’s kinematic dexterity, dynamic performance, and adaptability to operational constraints. These parameters collectively enable task-specific optimization from precision manipulation to terrain traversal. Key morphological categories include anthropomorphic robots (humanoids), zoomorphic (bioinspired) robots, wheeled robots, legged robots, and domain-specialized robots. These morphologies are tailored for unique operational media, including aerial drones (rotary/fixed-wing), underwater gliders, and swarm robots with decentralized coordination architectures.

3.2.4. Domain-Based Classification of Robots [4,37]

Robotic systems can also be grouped according to their application domains, which reflect their specific operational environments and functional roles. For example, industrial robots excel in factory settings and perform repetitive and precise tasks such as welding, painting, and handling materials. Service robots assist in everyday environments, including hospitals, homes, and public spaces, by providing support for cleaning, deliveries, healthcare, or customer interaction. Exploration robots such as drones, submarines, and planetary rovers are built for extreme or unreachable locations. In contrast, military robots are designed for defense-related missions such as surveillance and bomb disposal. Agricultural robots contribute to modern farming using automated monitoring and harvesting systems. Finally, social and personal robots focus on interacting with humans in roles ranging from educational to emotional. This classification illustrates how robotic technologies are customized to meet the demands of specific sectors and real-world challenges.

3.2.5. By Enabling Technology [3]

Robotic systems can be classified by their enabling technologies, revealing seven key domains that define their evolving capabilities and innovation paradigms.

(i)

Advanced Sensor Integration: Modern robotic systems rely on multi-modal sensor arrays to achieve environmental cognition. Cutting-edge technologies, including LiDAR, stereoscopic vision systems, and biomimetic tactile sensors, enable millimeter-scale spatial resolution and multispectral perception [38,39].

(ii)

Industry 4.0 and smart factories: Industry 4.0 integrates robotics with AI, IoT, and big data to enable real-time analytics, predictive maintenance, and adaptive automation in smart factories [40,41]. Collaborative robots (cobots) enhance flexibility and safety by working alongside humans in precise and ergonomically demanding tasks.

(iii)

AI-driven robotics leverages machine learning, computer vision, and decision-making algorithms to enhance adaptability. AI-driven robots can interpret sensor data, recognize patterns, and adapt to changing scenarios [42].

(iv)

IOT: The Internet of Robotic Things (IoRT) merges robotics with IoT for real-time data exchange, remote control, and predictive analytics in logistics and smart infrastructure [43]. 5G and blockchain enhance latency and data security, whereas Fog Robotics reduces cloud reliance and hardware costs in distributed simultaneous localization and mapping (SLAM) [44].

(v)

Soft Robotics: Soft robotics uses compliant materials such as elastomers and hydrogels, for safe and adaptive interaction with complex environments [45]. Ideal for medical, assistive, and rehabilitation applications, it also supports inspection and repair in hazardous settings such as nuclear facilities and aero-engines [46].

(vi)

Swarm robotics: Inspired by natural systems such as ant colonies, it enables large groups of simple robots to coordinate via decentralized control and local communication. Through self-organization and redundancy, these systems achieve scalable and resilient collective behaviors [47,48].

(vii)

Sustainable Robotics: Robotics supports the UN SDGs through innovations such as AI-powered waste-sorting robots that enhance recycling and promote circular economies [49]. In smart cities, IoT-integrated robots enable energy optimization, traffic control, air quality monitoring, and driving data-informed urban sustainability [50].

(viii)

Agricultural Robotics [51,52]: Robotic systems are transforming agriculture by enabling precision farming, a data-driven method that boosts productivity while reducing environmental impact. Throughout the crop lifecycle, from soil preparation to harvesting, autonomous tractors, drones, and robotic sprayers leverage sensors, GPS, and AI for targeted operations. For instance, drones detect diseases or pests early, whereas robot seeders and sprayers precisely deliver inputs and minimize waste.

4. Robotics in Research

Robotic research is the cornerstone of modern technological advancement, transforming industries, and tackling pressing global challenges. Robotics is reaching an elevated level of maturity and continues to benefit from advances and innovations in enabling technologies. Recent breakthroughs have propelled robots to become increasingly agile, adaptable, and intelligent, thereby expanding their capabilities across diverse applications [53,54]. This section highlights several key areas of robotics research and showcasing innovations that shape the future of this field. Table 2 lists examples of robots developed for various applications, each originating from a distinct research initiative.

4.1. Grasping and Manipulation [55,56,57,58]

Grasping and manipulation are fundamental to enable robots to interact in diverse and dynamic environments. Research has focused on improving dexterity, precision, and adaptability through innovations such as soft robotics and bio-inspired grippers, allowing for the safe handling of objects with varying shapes and fragility. Challenges span the design and control of robotic hands, including the gripper architecture, actuation, and transmission systems. Sensing technologies enhance performance, whereas anthropomorphic grippers replicate human-like dexterity. The integration of computer vision and machine learning has significantly improved perception and grasp planning in unstructured settings. These advances support real-world applications in manufacturing, logistics, and healthcare. Examples are assembling parts, packaging, preparing food, folding laundry, and assisting in surgery, thereby showcasing the growing versatility of robotic systems.

4.2. Motion Planning and Control [59,60,61]

Motion planning and control are critical for autonomous robotics, as they allow systems to navigate and perform tasks in dynamic environments. This process integrates perception, state estimation, and planning with core components such as localization, mapping, and world modeling, providing essential situational awareness. Advanced path planning and control techniques ensure precise, efficient, and collision-free movements. Key approaches include SLAM, probabilistic and sampling-based planners such as rapidly exploring random trees (RRT) and probabilistic roadmaps (PRM), and trajectory optimizers such as CHOMP and STOMP. Control strategies include model predictive control (MPC), kinematic and dynamic modeling, reinforcement learning, inverse kinematics, and visual servoing. These tools collectively enable stable and adaptive autonomy in both structured and unstructured environments.

4.3. Robot Vision, Sensing, and Perception [62,63]

Robot vision and perception enable intelligent interactions with the environment by acquiring, processing, and interpreting sensory data [54]. Robots use proprioceptive sensors (e.g., encoders, IMUs, and force/torque sensors) to monitor internal states and exteroceptive sensors (e.g., RGB cameras, LiDAR, radar, and sonar) to gather external data.

Perception relies on image processing, computer vision, and sensor fusion techniques for tasks such as object recognition, scene understanding, and motion tracking. These functions support robust environmental modeling which is essential for decision-making in unstructured contexts such as industrial automation and autonomous navigation. Recent advances have integrated AI-driven perception systems that are capable of semantic mapping and adaptive behavior. Applications range from collaborative robots in manufacturing to search-and-rescue drones and precision medical systems in which real-time interpretation and contextual awareness are critical.

4.4. Human–Robot Interaction [64,65,66,67]

HRI focuses on optimizing communication and collaboration between humans and robots, especially as robotic systems are integrated into the manufacturing, healthcare, and service domains to ensure intuitive, safe, and effective interaction. Advancements in natural language processing, gesture recognition, and affective computing have enhanced the ability of robots to interpret human inputs and respond appropriately. These technologies improve ergonomics by aligning robot behavior with human expectations, reducing fatigue, and improving usability.

HRI blends insights from robotics, AI, human–computer interaction, and psychology to develop socially aware robots. Robots such as Probo, NAO, and RAMCIP assist vulnerable populations by using affective computing to detect emotions through facial expressions, voice, and body language, adjusting interactions accordingly [68]. In industry, collaborative robots (cobots) improve productivity while ensuring worker safety. In healthcare, HRI supports devices, such as smart wheelchairs, prosthetics, and exoskeletons. Augmented reality interfaces further enhance interactions, enabling immersive and therapeutic applications.

4.5. Sensing and Actuation Technologies [69,70,71]

Sensors and actuators form the sensory-motor backbone of robotic systems. Their design, integration, and control dictate how robots perceive their environment and execute physical tasks. As robotics evolves toward greater autonomy and human–robot interaction, these technologies are undergoing rapid innovation.

4.5.1. Sensors

Robotic sensing systems are broadly classified into:

• Proprioceptive sensors, such as encoders, IMUs, electromyography (EMG), and force-torque sensors, provide internal state feedback (e.g., joint angles, orientation, andmotor loads).
• Exteroceptive sensors, such as LiDAR, stereo cameras, depth sensors, radar, and tactile arrays, deliver information about the external environment.

Emerging technologies are pushing boundaries:

• Event-based vision sensors capture pixel changes with microsecond latency, ideal for fast dynamic tasks like drone flight or sports robotics.
• Quantum sensors, including atomic interferometry-based accelerometers and magnetometers, offer unmatched precision in inertial navigation and SLAM.
• Neuromorphic sensors mimic biological sensing and are optimized for energy-efficient edge perception in swarm and micro-robotics applications.
• Flexible e-skin sensors enable conformal surface sensing for wearable robots and safe human–robot interaction.

4.5.2. Actuators

Robotic actuation systems convert control signals into motion or force. Key categories include the following:

• Electric actuators, particularly brushless DC motors and servos, are dominant in industrial arms and mobile platforms owing to their precision and efficiency.
• Pneumatic and hydraulic actuators offer high power density and are often used in legged or bioinspired robots where compliance and strength are required.
• Soft actuators, made of elastomers, shape-memory alloys, and dielectric elastomers, are used in rehabilitation, surgical robotics, and wearable systems.

4.5.3. Recent Trends and Research

Current research explores hybrid actuation systems that combine rigid and soft components for an adaptable morphology. Modular and reconfigurable actuator–sensor units are being designed to simplify robot customization. Closed-loop sensorimotor systems are increasingly being controlled via reinforcement learning and neuromorphic architectures to support agile, robust, and adaptive behaviors.

4.5.4. Future Outlook

Future directions include the following:

• Self-healing actuators and sensors for long-term operation in remote or hazardous environments.
• Biohybrid systems, integrating living tissues with artificial components.
• Integrated perception–action chips, which co-locate sensing, computation, and actuation for real-time responsiveness in microrobotics.
• Microactuators, as robotic systems continue to evolve toward higher precision, miniaturization, and bio-integration, the development of micro-actuators is gaining considerable momentum. These actuators are often based on electroactive polymers (EAPs), shape-memory alloys (SMAs), piezoelectric materials, or even microfluidics and offer high responsiveness and adaptability at the micro/nano and sub-scales. Their compact form makes them particularly suitable for minimally invasive surgical tools, micro-grippers, bio-inspired robots, and implantable robotic systems.

The fusion of intelligent sensing and advanced actuation will be instrumental in realizing next-generation robotic systems capable of safe, responsive, and context-aware interactions with the physical world.

4.6. Quantum Robotics

Quantum technologies increasingly influence the future of robotics by introducing novel computational and sensing capabilities. Quantum computing, which leverages qubits to perform parallel computations, offers significant advantages in solving complex problems such as route optimization and machine learning tasks, enabling faster and more efficient robotic decision-making [72]. In parallel, quantum sensing technologies provide ultra-sensitive navigation capabilities through quantum-enhanced position and magnetic field sensors. These advancements promise to revolutionize robotic performance in GPS-denied or magnetically complex environments [72]. Moreover, the application of quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), has shown potential for optimizing coordination and control in multi-agent systems. It has also improved safety, robustness, and reliability in autonomous robotic applications [73].

The latest research and experimentation of quantum-enhanced robotics, though in the early stages, introduced quantum-inspired optimization techniques for multi-robot coordination. The improved quantum optimization algorithm has shown superior performance in combinatorial tasks such as path planning for single and swarm robotics [74]. Another real-world experimental implementation is a compact quantum sensing system developed by Aquark Technologies, a University of Southampton spinout, which has recently undergone navigation trials onboard the Royal Navy’s HMS Pursuer [72]. The same system, weighing less than 10 kg, was successfully integrated into the quadcopter drone. The company employs a patented laser cooling technique that enables the construction of cold atom quantum sensors without relying on magnetic fields, significantly reducing the device size, weight, power requirements, and cost.

4.7. Neural Morphology Computing

Neural morphology computing (NMC) represents a paradigm shift in robotics, moving beyond traditional centralized control by tightly integrating neural-inspired information processing (“neural”) with the physical structure and dynamics of the robot’s body (“morphology”) to achieve embodied intelligence. This approach posits that intelligence emerges not only from a robot’s controller, but also from interactions between its computational substrate (often neuromorphic hardware or spiking neural networks) and the physical properties of its mechanical form—such as material compliance, limb geometry, or environmental interactions [75].

Inspired by the human brain architecture, neural networks facilitate parallel processing, allowing robots to adapt and learn from their environments more effectively. Recent advances in neuromorphic computing have introduced energy-efficient, brain-inspired models of computation that offer promising capabilities for robotics. Neuromorphic processors such as Intel’s Loihi 2 and BrainChip’s Akida support spiking neural networks (SNNs), enabling real-time sensory processing and control with ultra-low power consumption more efficienly than conventional architectures [76,77]. For instance, Intel’s Loihi neuromorphic processor demonstrated fully neuromorphic vision and control for autonomous drone flight [76]. Another practical example is the deployment of BrainChip’s Akida to create a neuromorphic E-Puck robot for obstacle avoidance applications [77].

4.8. Applications

Robots are transforming industries and improving lives across diverse sectors. In manufacturing, robotic arms and cobots perform assembly, welding, and quality control with high precision and efficiency [78]. Autonomous robotics underpins Industry 4.0, driving automation, adaptability, and innovation in smart factories [79].

In healthcare, robots enhance surgical precision and support rehabilitation through prosthetics and assistive devices [80]. Agricultural robots, including drones and autonomous vehicles, automate planting, spraying, and harvesting and improve sustainability and productivity [81].

Logistics benefits from AGVs, drones, and mobile robots for warehouse automation, inventory tracking, and last-mile delivery [82]. In disaster response, robots, such as UAVs and underwater systems, aid in search-and-rescue, hazard assessment, and real-time situational awareness [83]. These applications highlight the pivotal role of robotics in addressing the complex industrial and societal needs.

Robotics research and education are advancing rapidly and are being supported by high-impact journals and prestigious conferences. These venues provide a platform for disseminating cutting-edge findings and fostering academic collaborations.

Table 3. Top robotics journals and their metrics.

Journal Title	CiteScore	2020–2023 Citations	% Cited	Publisher
Science Robotics	30.6	9480	81	American Association for the Advancement of Science
Annual Review of Control, Robotics, and Autonomous Systems	28.3	2489	99	Annual Reviews Inc.
Robotics and Computer-Integrated Manufacturing	24.1	14,118	97	Elsevier
International Journal of Robotics Research	22.2	5921	84	SAGE
Soft Robotics	15.5	4976	88	Mary Ann Liebert
Journal of Field Robotics	15.0	4207	81	Wiley
IEEE Transactions on Robotics	14.9	11,685	84	IEEE
Robotics: Science and Systems	12.0	3230	94	MIT Press
International Journal of Social Robotics	9.8	4503	83	Springer Nature
IEEE Robotics and Automation Letters	9.6	44,648	83	IEEE
Robotics and Autonomous Systems	9.0	5741	84	Elsevier
IEEE Robotics and Automation Magazine	8.8	1673	76	IEEE
Cognitive Robotics	8.4	565	72	KeAi Communications
IEEE Transactions on Medical Robotics and Bionics	6.8	2483	81	IEEE
Robotics	6.7	3747	78	MDPI
Frontiers in Robotics and AI	6.5	7497	78	Frontiers Media
Journal of Robotics and Control (JRC)	6.3	1980	82	Universitas Muhammadiyah Yogyakarta
Intelligent Service Robotics	5.7	1001	78	Springer Nature
Journal of Mechanisms and Robotics	5.6	2510	87	ASME
International Journal of Medical Robotics and Computer Assisted Surgery	5.3	2631	83	Wiley
Frontiers in Neurorobotics	5.2	3968	70	Frontiers Media
Advanced Robotics	4.1	1747	69	Taylor & Francis
Journal of Micro and Bio Robotics	3.8	139	59	Springer Nature
International Journal of Intelligent Robotics and Applications	3.8	662	79	Springer Nature
Journal of Robotics	3.7	626	77	Wiley
Biomimetic Intelligence and Robotics	3.7	223	65	Elsevier
IET Cyber-systems and Robotics	3.7	407	66	Wiley
International Journal of Humanoid Robotics	3.5	350	75	World Scientific
International Journal of Robotics and Control Systems	3.1	476	72	ASCEE
Springer Proceedings in Advanced Robotics	2.6	1774	59	Springer Nature
Journal of Robotics and Mechatronics	2.2	1194	62	Fuji Technology Press

and

Table 4. Major robotics conferences and their focus areas.

Conference Name	Organizer	Focus	Frequency
IEEE International Conference on Robotics and Automation (ICRA)	IEEE Robotics and Automation Society	Robotics advancements, including AI, control, perception, and autonomous systems.	Annual
Robotics: Science and Systems (RSS)	RSS Foundation	Theoretical robotics, including ML, computer vision, and motion planning.	Annual
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)	IEEE Robotics and Automation Society, RSJ	Intelligent systems, sensing, AI, robot manipulation, and multi-robot systems.	Annual
International Symposium on Robotics Research (ISRR)	RSJ, Springer	Advanced robotics methodologies and fundamental research.	Biennial
Conference on Robot Learning (CoRL)	ML and Robotics Community	Robotics and machine learning, including reinforcement learning, perception, and planning.	Annual
International Conference on Humanoid Robots (Humanoids)	IEEE Robotics and Automation Society	Humanoid robotics, locomotion, HRI, and social robotics.	Annual
International Conference on Automation Science and Engineering (CASE)	IEEE Robotics and Automation Society	Industrial automation, robotics systems, and smart manufacturing.	Annual
International Conference on Social Robotics (ICSR)	Springer, various institutions	Social robotics, human–robot interaction, and ethical concerns.	Annual
International Conference on Advanced Robotics (ICAR)	IEEE Robotics and Automation Society	Emerging and innovative concepts in robotics.	Biennial
European Robotics Forum (ERF)	EU Robotics Association	Robotics research, industry collaboration, and policy development.	Annual
Robotics in Education (RiE)	International RiE Committee	Educational robotics, curriculum design, pedagogical tools, STEAM education	Annual

list the most influential journals and conferences in this field, based on SCOPUS [84] and Google Scholar [85].

5. Robotics in Education

The rising global demand for skilled roboticists has driven educational initiatives that focus on building expertise in this rapidly evolving field. At the university level, robotic integration has transformed learning by promoting innovation, entrepreneurship, and student engagement. Offering a hands-on, interdisciplinary approach, robotics bridges theory and application, aligning with the growing need for industry-ready skills [86]. Universities are central to this shift, serving as hubs for both education and advanced robotics research [87]. The inclusion of robotics in the curriculum fosters the next generation of innovators by developing essential technical and problem-solving abilities.

Robotics has become a powerful educational tool across all academic levels, fostering interdisciplinary competencies in STEM fields [88]. Early K–12 exposure to specialized undergraduate and postgraduate programs enhances creativity, problem-solving skills, and experiential learning. Educational robotics supports cognitive development while aligning with evolving workforce demands. Its integration can be structured into three key stages: K–12 education, undergraduate education, and postgraduate and research-oriented education.

5.1. K-12 Education [89,90]

Educational robotics significantly enriches K–12 learning by fostering creativity, problem-solving, and technical skills. It introduces students to robot design and programming, promoting logical thinking, spatial perception, and hands-on learning. Robotics also enhances collaboration, self-confidence, and interdisciplinary learning across subjects such as math, science, and art.

As part of STEAM education, robotics equips learners with practical knowledge and engages them in competitions such as the FIRST LEGO League, VEX IQ, RoboCup Junior, and World Robot Olympiad. These events cultivate innovation, teamwork, and technical proficiency.

5.1.1. Educational Robotics Tools and Platforms

Robotics education at this level uses a range of hardware and software:

Construction Kits:LEGO^® Technic, TETRIX^®, and VEX kits allow students to build functional robots.

Sensors and Actuators: Infrared, ultrasonic, and tactile sensors paired with motors and servos bring interactivity into projects.

Microcontrollers: Platforms such as Arduino, LEGO Mindstorms EV3, VEX Cortex, and Micro:bit introduce the coding and hardware integration.

5.1.2. Software and Programming Platforms

Block-Based Programming: Tools such as Scratch, Blockly, and RoboBlockly offer intuitive visual coding environments for beginners.

Text-Based Programming: More advanced learners use Python, Arduino IDE, or hybrid tools such as MakeCode for deeper engagement.

Educational robotics delivers a dynamic way to teach STEM, combining theory with hands-on practice and fostering skills essential for the digital age.

5.2. Higher Education

Robotics is revolutionizing industries, from manufacturing and healthcare to autonomous systems and AI. This field has witnessed exponential growth, necessitating an advanced workforce capable of innovating and implementing robotic systems [2]. Higher education institutions play a pivotal role in the development of skilled professionals through structured undergraduate and postgraduate programs. Here, we discuss the role of robotics education at different academic levels, focusing on curriculum, research opportunities, and industry collaboration.

Robotics-related topics are offered either in specialized robotics programs (BSc or MSc) or within other engineering disciplines, with Mechatronics Engineering being a notable example.

Table 5. Robotics education: categories, course fields, and corresponding topics.

Category	Course Field	Topics
Core Topics	Mathematics	Linear Algebra, Calculus (differential and integral), Probability and Statistics.
	Physics	Mechanics, Electronics, Materials Science,
	Computer Science	Algorithms, Programming (Python, C++, ROS), AI and Machine Learning, Embedded Software
	Engineering Principles	Electrical Engineering, Mechanical Engineering, Systems Integration
Specialized Topics	Robot Kinematics and Dynamics	Forward/Inverse Kinematics, Motion Planning, Multi-body Dynamics
	Control Systems	Feedback Control (PID), Nonlinear/Adaptive Control, State Estimation, Kalman Filters
	Sensing and Actuating	Sensors (LiDAR, Cameras), Computer Vision, Sensor Fusion, motors, servos, pneumatics
	AI for Robotics	Path Planning (A*, RRT), Reinforcement Learning, NLP, Swarm Intelligence
	Human–Robot Interaction	User Interfaces, Collaborative Robots, Ethical and Social Considerations
	Embedded Systems	Microcontrollers (Arduino, Raspberry Pi), Real-Time Operating Systems (RTOS), communication
Emerging Topics	Soft Robotics	Bio-Inspired Robotics, Design and Control of Soft Materials
	Autonomous Systems	Autonomous Vehicles, Marine Robotics, Space Robotics, SLAM
	Robotics and AI Integration	Deep Learning, Cognitive Robotics, Ethical AI
	Additive Manufacturing	3D Printing, Customizable Components
	Cybersecurity	Securing Robotic Systems, Cyberattack Prevention
Research and Practical Skills	Hands-On Prototyping	Building and Programming Robots, Debugging, Testing, Capstone Projects
	Simulation and Modeling	Robotics Simulators (Gazebo, Webots), Digital Twin Technology
	Communication and Teamwork	Collaborative Projects, Technical Writing, Multidisciplinary Team Management
Ethical and Societal Topics	Ethical Design	Responsible Robotics, Ethical Use
	Environmental Impact	Sustainability in Robotics
	Social Implications	Workforce Displacement, Addressing Societal Challenges

outlines the key robotics topics and courses categorized by specific areas of focus, and

Table 6. Comparison of undergraduate and graduate robotics programs.

Aspect	Undergraduate Programs (BSc)	Graduate Programs (MSc)
Program Names	BSc in Robotics Engineering, Mechatronics, Automation and Robotics, or Computer Science (Robotics Focus)	MSc in Robotics, AI and Robotics, Autonomous Systems, or Mechatronics and Robotics
Focus Areas	Fundamentals of robotics, mechanical/electrical/software engineering, hands-on design and prototyping	Advanced robotics concepts, AI and machine learning in robotics, automation, control systems, and autonomy
Duration	3–4 years	1–2 years
Entry Requirements	High school diploma (background in math, physics, programming optional)	BSc in robotics, mechatronics, or related fields; sometimes GRE/GMAT, research experience, or portfolio required
Key Courses	Introduction to Robotics, Sensors and Actuators, Programming for Robotics, Robot Kinematics and Dynamics, Control Systems	Advanced Robotics Programming, Machine Learning for Robotics, Robot Perception, Advanced Mechatronic Design, Research Methodology
Skills Developed	Engineering fundamentals, programming, robot design, problem-solving, teamwork	Advanced research, AI and autonomy specialization, system integration, innovation
Career Opportunities	Entry-level roles: Robotics Technician, Automation Engineer, Embedded Systems Developer	Advanced roles: Robotics Scientist, AI Specialist, Control Systems Engineer, Researcher, Academic positions
Industries	Manufacturing, automotive, healthcare, logistics	Advanced R&D, aerospace, defense, high-tech healthcare robotics, academia
Research Component	Minimal; hands-on and coursework-focused	Strong emphasis on research; often requires a thesis or project
Capstone/Thesis	Capstone project with practical applications	Research thesis or advanced project contributing to the field
Global Popularity	High demand in developing nations and entry-level robotics markets	Preferred in research-intensive institutions and high-tech industries
Future Pathways	Further education (MSc) or immediate industry entry	Doctoral studies (PhD) or leadership roles in robotics research and innovation

provides a comparison between undergraduate and graduate robotics programs.

5.2.1. Undergraduate Robotics Education [16,17,91]

• Curriculum Structure

Undergraduate robotics programs are offered either as a standalone B.Sc./B.Eng. degrees or as concentrations within fields such as mechatronics or computer engineering, are structured to reflect the interdisciplinary nature of robotics [91,92]. These programs typically consist of four core components: foundational disciplines, core robotics courses, elective specializations, and capstone projects [16,17].

• Key Components

(Table 5).

• Core Foundations: Mathematics, physics, computer science, and systems engineering provide the analytical basis for robotics. These are typically covered during the first two years [92].
• Core Robotics Courses: Covering robot kinematics, dynamics, control systems, and AI, these modules emphasize lab work with tools such as ROS, MATLAB, and Arduino [17].
• Electives: Depending on the institutional offerings, students may specialize in areas such as soft robotics, HRI, or IoT-based systems. Interdisciplinary tracks include ethics, design, or business modules [91].
• Capstone Project: This final-year experience involves designing a full robotic system, such as autonomous drone or robotic arm, often in collaboration with industry or university laboratories. The deliverables include documentation, prototypes, and seminar presentations [16].

Table 7. Representative robotics textbooks categorized by focus area.

Category	Textbook Title and Authors	Key Focus/Typical Use Cases
General Robotics	Introduction to Robotics: Mechanics and Control John J. Craig	Introductory textbook covering mechanical foundations, actuators, sensors, and basic control. Widely used in undergraduate courses.
	Modern Robotics: Mechanics, Planning, and Control Kevin M. Lynch and Frank C. Park	Balanced coverage of theory and implementation, ideal for undergrad and grad courses.
	Robotics: Control, Sensing, Vision, and Intelligence K.S. Fu, R.C. Gonzalez, C.S.G. Lee	Comprehensive introduction to robotics, including control systems, sensors, vision, and artificial intelligence. Suitable for both beginners and advanced learners.
	Springer Handbook of Robotics Edited by Bruno Siciliano and Oussama Khatib	Comprehensive reference that spans foundational theories, robot kinematics, dynamics, control, sensing, learning, HRI, and ethics. Ideal for graduate-level study, multidisciplinary research.
Robot Kinematics and Dynamics	Robotics: Modelling, Planning and Control Bruno Siciliano et al.	In-depth treatment of kinematics, dynamics, control, and trajectory planning; suitable for advanced learners.
	Robot Dynamics and Control Mark W. Spong et al.	Focus on dynamic modeling and feedback control techniques.
Autonomous Robots and Mobile Robotics	Introduction to Autonomous Mobile Robots Roland Siegwart, Illah Nourbakhsh, and Davide Scaramuzza	Core principles of mobility, navigation, localization, and perception for mobile robots.
	Probabilistic Robotics Sebastian Thrun, Wolfram Burgard, and Dieter Fox	Covers probabilistic models, SLAM, Bayesian filtering; ideal for autonomous systems research.
Computer Vision and Perception in Robotics	Computer Vision: Algorithms and Applications Richard Szeliski	Comprehensive guide to vision algorithms used in robotics and AI applications.
	Multiple View Geometry in Computer Vision Richard Hartley and Andrew Zisserman	Focuses on 3D vision, structure from motion, stereo vision—used in advanced robotics perception.
	Robotics, Vision and Control: Fundamental Algorithms in MATLAB/Python Peter Corke	Practical integration of robotics and computer vision algorithms, with detailed implementations in MATLAB and Python. Covers kinematics, visual servoing, and perception pipelines. Widely used in labs, projects, and applied robotics courses.
AI and Machine Learning for Robotics	Introduction to AI Robotics Robin R. Murphy	Broad AI textbook, foundational for reasoning, planning, and learning in robotics.
	Artificial Intelligence: A Modern Approach Stuart Russell and Peter Norvig	Broad AI textbook, foundational for reasoning, planning, and learning in robotics.
	Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Aurélien Géron	Practical guide to implementing machine learning and deep learning algorithms using Python libraries. Covers neural networks, CNNs, RNNs, and reinforcement learning. Widely used in robotics projects involving perception, control, and adaptive behaviors.
Cognitive Robotics	Cognitive Robotics Angelo Cangelosi and Minija Tamosiunaite	Explores cognitive architectures, learning, reasoning, and human–robot interaction.

presents the selection of the most widely used robotics textbooks across academic and professional settings, along with their primary focus areas and common applications.

• Industry Collaboration and Internships

Partnerships with industry leaders expose students to real-world applications. Companies such as Boston Dynamics, KUKA, and ABB Robotics offer internship programs that bridge the gap between academia and the industry.

5.2.2. Postgraduate Robotics Education [87,88]

Postgraduate robotics education builds on undergraduate foundations to provide a focused, research-intensive pathway through M.Sc, M.Eng, and Ph.D. programs. These programs cultivate deep technical expertise and prepare graduates for leadership in academia, industry, and research institutions [90,91].

• Curriculum and Specialization

Master’s programs emphasize advanced robotics topics such as nonlinear control, robot learning, optimization, and SLAM. Students often pursue electives in specialized tracks, including bio-inspired robotics, HRI, swarm intelligence, and medical robotics [88]. This enables engagement with the frontier technologies and interdisciplinary challenges.

• Research and Thesis Integration

A central component of postgraduate robotics education is the research thesis, through which students engage in comprehensive literature analysis, design and implement experimental or simulation-based systems, and aim to generate publishable research outcomes. Doctoral candidates further extend this process by undertaking long-term, original investigations, often involving interdisciplinary or inter-institutional collaboration [87].

• Industry Collaboration and Applied Research

Many programs integrate applied research through internships, sponsored theses, and partnerships with laboratories such as MIT CSAIL or Stanford’s Robotics Lab. These experiences help align academic learning with real-world applications and industrial needs (see

Table 8. Examples of postgraduate robotics programs and their research partnerships.

University	Program	Partner Research Institutions/Focus Areas
Carnegie Mellon University (USA)	M.S./Ph.D. in Robotics	Robotics Institute, National Robotics Engineering Center; collaborations with NASA, DARPA, Google; focus on autonomous systems, HRI, soft robotics
ETH Zurich (Switzerland)	M.Sc. in Robotics, Systems, and Control	ETH Robotics Systems Lab, Autonomous Systems Lab; aerial robotics, legged robots, mechatronic systems
Technical University of Munich (Germany)	M.Sc. in Robotics, Cognition, Intelligence	Munich Institute of Robotics and Machine Intelligence (MIRMI); partnerships with Siemens, BMW; cognitive robotics, AI
University of Tokyo (Japan)	Graduate Program in Information Science	JSK Robotics Lab; cooperation with RIKEN, AIST; humanoid robotics, soft robotics
Imperial College London (UK)	M.Sc. in Medical Robotics	Hamlyn Centre for Robotic Surgery; NHS collaborations, Intuitive Surgical; surgical robotics, medical AI
National University of Singapore (NUS)	M.Sc./Ph.D. in Robotics	Advanced Robotics Centre; collaborations with A*STAR and global institutions; smart mobility, assistive robotics
Tsinghua University (China)	M.E./Ph.D. in Control Science and Engineering (Robotics Track)	Partners with the Institute of Automation, Chinese Academy of Sciences (CASIA); focus on intelligent robotics, computer vision, human–robot interaction

).

• Academic Development and Professional Skills

Beyond technical mastery, postgraduate programs emphasize academic literacy, communication, and leadership. Common components include the following.

• Research methods and scientific writing.
• Presentation skills and conference participation.
• Ethics in robotics research.
• Grant writing and innovation management.

Graduate students are often encouraged, or required, to present at international conferences, as shown in Table 4, and publish their work in peer-reviewed journals, as shown in Table 3. These experiences foster not only scholarly maturity but also professional confidence and networking.

In summary, postgraduate robotics education offers a platform for intellectual specialization, original research, and high-level engagement with industry and society. As robotics continues to permeate every facet of modern life, postgraduate programs are vital in shaping the next generation of roboticists capable of leading in both the technological and ethical dimensions.

6. Career Prospects in Robotics

The rapid proliferation of robotics across industrial, commercial, and societal domains has significantly expanded career opportunities for robotics graduates. Both undergraduate and postgraduate robotics degrees provide access to a wide spectrum of roles that blend engineering, computer science, artificial intelligence, and human-centric designs. Career prospects span not only traditional engineering sectors, but also emerging industries where intelligent automation and autonomous systems are critical.

6.1. Industry Roles and Sectors

Robotics graduates are in high demand across a variety of industries, including, but not limited to:

• Manufacturing and Industrial Automation: Development of robotic arms, assembly automation, and smart factories using Industry 4.0.
• Healthcare and Medical Robotics: Design of surgical robots, rehabilitation devices, prosthetics, and assistive technologies.
• Autonomous Vehicles and Drones: Roles in perception, navigation, and control of land, aerial, and underwater autonomous systems.
• Agriculture and Environmental Robotics: Automation of precision farming, crop monitoring, and conservation applications.
• Logistics and Warehouse Automation: Robotics solutions for inventory management, package handling, and autonomous delivery.
• Consumer Robotics and AI Assistants: Development of domestic robots, smart devices, and embodied AI interfaces.

Job titles for graduates may include Robotics Engineer, Automation Specialist, Machine Learning Engineer, Mechatronics Engineer, AI Developer, or Research Scientist in robotics-related domains.

6.2. Academic and Research Careers

Graduates with advanced degrees (M.Sc. and Ph.D.) often pursue research-intensive careers in academia, government laboratories, and corporate R&D. These roles emphasize the following:

• Fundamental research in robotic perception, control, learning, and human–robot interaction.
• Development of novel robotic systems and algorithms for scientific exploration or societal benefit.
• Contribution to interdisciplinary initiatives in neuroscience, cognitive science, or ethics.
• Mentorship and teaching in robotics, engineering, and computer science programs.

6.3. Entrepreneurship and Startups

The dynamic growth of the robotics ecosystem has led to an increase in entrepreneurial activity. Many graduates, especially those from research-focused or innovation-centered programs, leverage their expertise to launch robotic start-ups. Common focus areas include the following.

• Service robotics (e.g., elder care, cleaning, delivery).
• Robotic platforms for education and STEM training.
• Niche automation tools for vertical industries (e.g., mining, construction).
• AI-enhanced robotics software and simulation tools.

University incubators, venture capital funding, and tech accelerators often support early-stage ventures by offering an alternative to traditional employment pathways.

6.4. Global Demand and Emerging Markets

The global demand for robotics professionals continues to grow, driven by strategic initiatives such as:

• Germany’s Industrie 4.0.
• Japan’s Robot Strategy.
• Republic of Korea’s Fourth Intelligent Robot Basic Plan.
• U.S. National Robotics Initiative.
• China’s Made in China 2025.
• Government Labs: Opportunities at NASA, CERN for large-scale robotics projects.

Robotics graduates benefit from a dynamic, multidisciplinary career landscape. Regardless of whether they pursue roles in industry, research, entrepreneurship, or policy, they are uniquely positioned to shape the next generation of intelligent systems that transform how we work, live, and interact with technology.

7. Robotics Education: Tools and Platforms for Experiential Learning

University-level robotics education blends software frameworks, hardware platforms, and simulators to link theory with hands-on experience, fostering systems thinking and cross-disciplinary collaboration. Below is an overview of the essential tools used in undergraduate and graduate curricula.

7.1. Software Tools

Robotics software supports algorithm design, simulation, system integration, and hardware abstraction.

Robot Operating System (ROS): Standard middleware in education and research, offering modular architecture, sensor/actuator integration, and Gazebo simulation support. ROS2 enhances real-time performance, security, and multi-platform use [93,94].

MATLAB/Simulink: This software is widely used for modeling, control design, and reinforcement learning. Its Robotics System Toolbox and Corke’s Robotics Toolbox support kinematics, trajectory planning, mapping, and localization [95,96].

Python & C++: Python excels in readability and machine learning support (TensorFlow, NumPy). C++ is crucial for real-time control and embedded applications [97,98].

OpenCV: Essential for computer vision tasks such as image processing, feature extraction, SLAM, and navigation [99].

Gazebo, CoppeliaSim, Webots: Physics-based simulators vital for validating algorithms safely. Gazebo integrates closely with ROS, and CoppeliaSim supports scripting and extensive robotic models [100].

7.2. Hardware Platforms

Tangible platforms help students to move from theory to implementation by enabling embedded systems, perceptions, and control practices.

Arduino & Raspberry Pi: Affordable microcontrollers and single-board computers for sensor interfacing, control workflows, and edge computing (e.g., camera and networking) [101].

NVIDIA Jetson: AI-focused modules (Nano, Xavier, Orin) with onboard GPUs that support deep learning, object detection, and real-time autonomy [102].

3D Printers: Tools like Ultimaker and Prusa enable custom part fabrication, rapid prototyping, and creativity enhancement [103].

Sensor and Perception Modules: LiDAR (RPLIDAR, Velodyne), IMUs (MPU-6050, BNO055), RGB-D cameras (Intel RealSense, Azure Kinect), ToF cameras, stereo vision, GPS, and force/torque sensors—used for SLAM, mapping, and interaction [104,105].

Industrial and Collaborative Arms: Platforms such as KUKA LBR iiwa, UR3/UR5, and ABB YuMi are used in advanced laboratories for teaching trajectory planning, control, and HRI within ROS ecosystems [106]. Table 2 lists additional examples of these platforms.

7.3. Educational Robotics Kits

Modular kits bridge affordable entry-level and advanced robotics learning.

Quanser Kits: High-fidelity manipulators, UAVs, and vehicles compatible with MATLAB, LabVIEW, and ROS—they support teaching control, mechatronics, and real-time systems [107].

TurtleBot3 and Husky UGV: ROS-enabled mobile bases ideal for SLAM, navigation, and perception—TurtleBot3 is compact and teaching-friendly. Husky supports field robotics [108].

LEGO SPIKE Prime/Mindstorms EV3: Graphic programming tools used in introductory engineering and outreach for teaching basic robotics principles [109].

Humanoid Platforms (OP3/Darwin-OP): Support bipedal locomotion, vision, and AI-based behaviors in advanced lab courses.

Dobot Magician/Niryo One: Desktop arms for teaching pick-and-place, vision, and PLC integration in laboratories.

VEX Robotics/TETRIX: Used in design courses and competitions to teach mechanical design, embedded programming, and teamwork.

Universities often integrate these resources through pipelines: design and simulate in ROS+Gazebo and then deploy them on TurtleBot3 or UR5. Open source ROS packages enhance collaborative and adaptable learning environments [110]. Although balancing costs and scalability poses challenges, adaptability of ROS continues to align educational platforms with industrial practices [94].

7.4. ROS

ROS is the de facto standard open source middleware for robotics, originally developed by Willow Garage for the PR2 platform [93] and is now maintained by Open Robotics [111]. Although ROS supports modular, reusable software across diverse platforms, its centralized communication model and limited real-time capabilities pose challenges. These limitations are addressed in ROS2, which adopts a Data Distribution Service (DDS)-based architecture to enhance scalability, reliability, and real-time performance [112].

8. Curriculum-Level Robotics Integration: Insights from the German Jordanian University

The integration of robotics into undergraduate education plays a pivotal role in fostering interdisciplinary learning, bridging the theoretical foundations with practical applications, and equipping students to address emerging technological challenges. At the Mechatronics Engineering Department of German Jordanian University, robotics has been strategically embedded throughout the curriculum through class projects, laboratory work, project-based courses, capstone projects, and robotics competitions. This comprehensive, hands-on approach not only enhances students’ technical proficiency, but also cultivates essential soft skills such as teamwork, innovation, and creative problem-solving.

8.1. Class Projects and Laboratory Work

Robotic concepts are embedded across core courses to bridge theoretical foundations with hands-on applications. This integration allows students to apply engineering principles in practical contexts through structured class projects. Examples include the following:

• Embedded Systems: Students program microcontrollers such as Arduino or Raspberry Pi to execute basic robotic tasks, including line-following and obstacle-avoidance robots.
• Control Systems: Student teams design and implement PID and state-space controllers for dynamic systems such as balancing inverted pendulums or stabilizing twin-rotor drones.
• AI: Students develop and deploy path-planning and decision-making algorithms using ROS on mobile robot platforms.
• Computer-Aided Engineering Courses: Courses involving MATLAB, Simulink, Simscape, LabVIEW, Python, and CAD tools support simulation, modeling, and robotic system design.

Dedicated robotics laboratories have been used to reinforce these concepts through experimentation. These facilities support a wide range of activities, including:

• Mechatronics Laboratories: Students integrate sensors, actuators, and microcontrollers to build functional robotic systems such as robotic arms.
• Industrial Robot Programming: Students gain hands-on experience programming various robotic manipulators, including KUKA, Mitsubishi, and Quanser.

Figure 1 illustrates the robotic tools used in both the coursework and laboratory projects. These tools support hands-on learning in control systems, embedded programming, real-time simulation, and mechatronics integration.

8.2. Project-Based Courses

Several courses within the curriculum are fully structured around open-ended robotics challenges, providing students with opportunities to engage in applied project-based learning. Examples include:

• Robotics Design Course: This course challenges students in designing and building autonomous robotic systems capable of operating in obstacle-rich environments. Emphasis is placed on hardware/software co-design, sensor integration, and real-time embedded control. An exemplary project involves the creation of agricultural robots that merge mobile platforms with robotic arms for tasks such as crop monitoring and leaf picking. These robots incorporate 3D-printed components, machine vision systems, and AI-based decision-making algorithms. As depicted in Figure 2 and Figure 3, students performed iterative testing of an autonomous agricultural robot, integrating line-following capabilities, manipulator control, and sensor–actuator feedback loops. This hands-on experience cultivates essential competencies in embedded systems, control theory, and precision agriculture—core pillars of contemporary robotics education.
• Quanser Autonomous Vehicles Research Studio: As shown in Figure 4, this specialized lab supports immersive, project-based learning in swarm and cooperative robotics. Students design and deploy multi-agent systems using autonomous ground vehicles and aerial drones. The platforms are equipped with high-resolution vision systems and precision sensors, enabling real-time experimentation with coordination strategies, distributed control algorithms, and collective intelligence. This environment fosters a hands-on understanding of complex system dynamics, scalability, and emergent behavior in swarm robotics applications.

As illustrated in Figure 5, the final seminar serves as a culminating academic event in which students present their project outcomes to their peers, faculty, and/or industry mentors. These presentations foster essential skills in technical communication, critical thinking, and reflective learning. The seminar provides a platform for showcasing prototypes, discussing design challenges, and receiving feedback, thereby reinforcing the academic rigor and practical relevance of the robotics curriculum.

8.3. Capstone Projects

Capstone projects constitute a cornerstone of the senior-level robotics curriculum, providing students with a rigorous framework to consolidate and apply their multidisciplinary knowledge to real-world, complex engineering problems. These projects foster innovation, systems-level thinking, and cross-functional teamwork, simulating the collaborative and problem-solving environments encountered in both the industry and research. Through the capstone experience, students engage in the entire engineering lifecycle, from problem definition and conceptual design to prototyping, integration, testing, and final presentation, thus preparing them for advanced roles in robotics, automation, and cyber-physical systems.

Two illustrative examples of recent capstone projects are highlighted below:

• Hybrid Ground/Aerial Robot: This project involved the development of a dual-modality robotic platform designed for long-endurance autonomous mobility across heterogeneous environments. As shown in Figure 6, the system integrates a wheeled ground vehicle with a quadrotor drone, thereby enabling terrain-adaptive navigation and aerial surveillance. Constructed using lightweight 3D-printed components and powered by an NVIDIA Jetson platform running ros2, the robot supports real-time SLAM, LiDAR-based localization, and multi-sensor fusion for perception-driven autonomy. This project exemplifies the advanced capabilities of embedded systems, multi-modal locomotion, and autonomous environmental mapping.
• Smart Factory Cyber-Physical System: Another capstone project focused on building a smart manufacturing prototype aligned with Industry 4.0. As depicted in Figure 7, the team designed an integrated cyber-physical platform that combine industrial automation, edge computing, and cloud-based monitoring. The key components included a KUKA KR 6 sixx R900 (Augsburg, Germany) industrial manipulator, Siemens PLC (Munich, Germany), vision-based inspection module, and web-connected interface. The robot was programmed using the KUKA Robot Language (KRL), and cloud integration was achieved via IBM Watson IoT using MQTT and REST protocols. This setup enabled automated drilling, inspection, and remote diagnostics. This project highlighted robotics as the central actuator within a smart factory environment, executing high-precision manipulation, responding to cyber-level decisions, and interacting with the physical production process. This demonstrated the students’ ability to implement secure, interoperable, and intelligent robotic systems in modern industrial contexts.

8.4. Robotics Competitions

In addition to formal coursework and capstone projects, students are strongly encouraged to engage in regional and international robotics competitions. These events provide a high-impact, experiential learning environment that cultivates creativity, resilience, and collaborative problem-solving. By simulating real-world constraints, such as time pressure, hardware limitations, and dynamic task requirements, competitions offer a valuable extension to traditional classroom instruction.

The scope of these activities spans a diverse array of challenges, including autonomous firefighting robots, maze navigation, robotic football, and precision line-following systems. Students also participate in modeling and simulation contests using MATLAB/Simulink, as well as algorithm-intensive events focused on artificial intelligence, computer vision, and embedded programming. These hands-on engagements allow participants to deepen their technical expertise, practice interdisciplinary integration, and stay abreast of current developments in robotics technologies.

Figure 8 and Figure 9 show student projects developed for robotics competitions, highlighting the applications of sensing, control, and autonomous decision-making in constrained and complex scenarios.

8.5. Discussion: The Role of Key Instructional Components in Robotics Education

A holistic robotics curriculum requires careful integration of diverse instructional components that progressively build student competencies, from foundational technical skills to advanced system-level thinking and innovation. At German Jordanian University, a tiered instructional design comprising class projects, laboratory work, project-based courses, capstone projects, and robotics competitions has proven effective in cultivating technically proficient, industry-ready graduates.

8.5.1. Class Projects

Class projects serve as the initial touchpoints for experiential learning. Embedded within core theoretical courses, these projects allow students to apply foundational concepts to embedded systems, controls, and artificial intelligence in a practical setting. They promote early engagement, reinforce classroom content, and introduce students to the basics of systems integration and debugging. The main challenges at this stage include variability in student preparedness and limitations in project complexity owing to time and resource constraints. Best practices include scaffolding projects to align with course objectives and incorporating modular assignments to ensure inclusivity for students with varying skill levels.

8.5.2. Laboratory Work

Laboratory work provides a structured environment for skill acquisition, offering hands-on experience by using sensors, actuators, microcontrollers, and commercial robotic arms. Labs reinforce theoretical knowledge while familiarizing students with standard industrial tools and protocols. These are critical for developing practical competencies in circuit integration, embedded programming, and robotic system configuration. One challenge is maintaining a balance between guided and exploratory learning. Effective lab design incorporates pre-lab exercises, structured assessments, and a gradual progression toward open-ended tasks to foster autonomy and problem-solving.

8.5.3. Project-Based Courses

Project-based courses bridge the gap between structured labs and open-ended design experience. These courses are built on robotic challenges that require iterative design, prototyping, and system-level integration. They develop higher-order cognitive skills such as critical thinking, teamwork, and design trade-off analysis. The iterative nature of these courses allows experimentation and refinement, mirroring real-world engineering processes. Challenges include ensuring equitable team participation and managing project scope. Best practices involve milestone-based grading, peer evaluation, and industry-relevant design briefs.

8.5.4. Capstone Projects

Capstone projects mark the culmination of the robotics learning journey, demanding the application of interdisciplinary knowledge to solve complex and often ill-defined problems. These projects simulate industrial- or research-level challenges and encourage innovation, technical depth, and professional accountability. Students engage in all phases of the engineering lifecycle, including documentation and presentation. A key challenge is ensuring sufficient project mentorship, access to specialized resources, and funding for projects. Successful capstone experiences are often supported by multi-disciplinary supervision, industry collaboration, and iterative project review.

8.5.5. Robotics Competitions

Participation in robotics competitions offers students a high-stakes, externally validated environment that drives both technical rigor and creative problem-solving. These events cultivate essential skills such as rapid prototyping, collaborative teamwork under pressure, and the design of robust, field-ready systems. Additionally, competitions expose participants to emerging technologies, provide opportunities for peer benchmarking, and facilitate professional networking. Despite challenges related to resource demands and team coordination, competitive settings foster experiential learning and innovation beyond traditional classrooms. To maximize educational value, institutions should promote inclusive team dynamics and provide comprehensive support through funding, laboratory access, and faculty mentorships.

8.5.6. Cumulative and Incremental Skill-Building

Structured layering of these instructional elements supports incremental skill development. Class projects introduce basic design thinking, laboratory work builds hands-on competence, project-based courses develop integration and teamwork skills, capstone projects foster systems-level thinking, and competitions enhance adaptability and real-world problem-solving. Collectively, this ecosystem of experiences ensures that students graduate with not only strong technical foundations, but also the soft skills necessary for success in the robotics industry and research.

Challenges and Best Practices

Although the integration of robotics has significantly enriched engineering education, several challenges must be addressed to sustain and scale its impact.

• Resource Limitations: The acquisition and maintenance of advanced hardware and software platforms can be cost-prohibitive. Strategic partnerships with industry leaders such as NVIDIA, Boston Dynamics, and Quanser are essential to provide access to state-of-the-art technologies and funding opportunities.
• Interdisciplinary Coordination: Robotics inherently spans multiple domains, including mechanical design, control systems, artificial intelligence, and embedded computing. Aligning learning outcomes and content delivery across departments requires an intentional collaborative curriculum planning.
• Rapid Technological Evolution: With continuous advancements in areas such as AI, soft robotics, and edge computing, curricula and laboratory infrastructure must be updated regularly to remain relevant and forward-looking.

To mitigate these challenges and maximize educational outcomes, the following best practices were identified:

• Industry–Academia Partnerships: Collaborations with industry not only provide mentorship and funding, but also expose students to real-world problem statements, tools, and workflows, enhancing the relevance of their education.
• Modular Curriculum Design: Structuring the curriculum in modular blocks facilitates progressive skill development, ranging from introductory topics, such as kinematics and embedded systems, to advanced subjects such as multi-agent autonomy and machine vision.
• Utilization of Open Source Tools: Leveraging open source platforms such as ROS, Gazebo, and TensorFlow democratizes access to robotics education and fosters a culture of experimentation and innovation.

The Mechatronics Engineering Department at German Jordanian University demonstrates a scalable and sustainable model for experiential and interdisciplinary learning by systematically embedding robotics into class projects, laboratory work, project-based courses, capstone projects, and competitive activities. This approach equips students with deep technical expertise and cultivates the adaptability, creativity, and collaborative mindset required to lead in the rapidly evolving fields of robotics and intelligent systems. Integrating robotics experiments into undergraduate education through class projects, project-based courses, and capstone projects creates a progressive learning pathway that aligns with academic and industrial needs. Each component builds upon the previous one, which gradually increases complexity and autonomy. By engaging in these activities, students gain practical skills, develop creative solutions, and prepare to address the challenges of tomorrow’s technological landscape. This framework not only enhances teaching effectiveness, but also inspires the next generation of robotics researchers and practitioners.

9. Conclusions and Future Directions

9.1. Conclusions

Robotics is transforming industries by integrating AI, embedded systems, and interdisciplinary engineering. This synergy enables advanced automation, precision, and adaptability, driving innovation in sectors ranging from manufacturing and healthcare to aerospace and autonomous transportion. By combining computational intelligence with optimized hardware and system design, robotics enhances efficiency and expands the technological frontiers.

This paper reviewed the evolution of robotics from early automata to intelligent systems and provided a taxonomy of robotic types and applications. Key research areas, such as motion planning, manipulation, and HRI have been explored, highlighting their growing impact. A major focus was robotics education, in which project-based learning, labs, and competitions nurtured creativity, systems thinking, and technical fluency across all academic levels. The integration of ROS into curricula enables modular hands-on learning in real-time control and algorithm development, thus supporting both education and innovation.

A case study from the German Jordanian University demonstrated a tiered robotics curriculum. Incorporating robotics into projects, labs, and capstones, the Mechatronics Engineering Department equips students with core competencies in embedded systems, AI, and control. This structured experiential model exemplifies how practical learning bridges theory with industry readiness.

While this review presents a comprehensive analysis of instructional practices in robotics education, highlighting approaches such as project-based learning, capstone courses, competitions, and lab experimentation, the quantitative evaluation of their effectiveness remains a limitation. As many of these pedagogical models have only recently been implemented, empirical outcome data (e.g., student learning gains, performance metrics, or longitudinal tracking) are not yet available. Section 8.5 offers a qualitative assessment, emphasizing both benefits and challenges. Future work will focus on collecting and analyzing multi-semester data to rigorously evaluate the educational impact and inform curriculum refinement through evidence-based practices.

9.2. Future Directions

The rapid evolution of robotics demands proactive anticipation of emerging paradigms. This section outlines seven critical trajectories shaping the next decade of robotics research and education, with an emphasis on specific implementation strategies, costs, expected outcomes, and potential obstacles.

9.2.1. Embodied AI and Neuromorphic Computing

Recent progress in deep learning and large language models (LLMs) has provided opportunities to develop robotic systems with human-like reasoning and environmental understanding [113]. However, these models require substantial computational power and may lack adaptability to resource-constrained environments.

A promising implementation path involves integrating neuromorphic processors with SNNs to enable real-time decision-making at the edge [10]. When combined with morphological computation, in which a robot’s physical structure contributes to computation, these systems offer improved energy efficiency, robustness, and adaptability. Future systems must address sim-to-real gaps through physics-aware learning frameworks [114] and a co-design methodology where control algorithms are developed in tandem with compliant robotic morphologies, such as soft limbs or tendon-driven actuators.

Anticipated outcome: Robots capable of real-time, adaptive behavior in unstructured environments (e.g., search-and-rescue, elder care) with lower power consumption, reduced controller complexity, and improved autonomy in unstructured environments.

Obstacles: Lack of mature development tools and limited standardization; hardware availability constraints due to limited off-the-shelf neuromorphic systems; high initial R&D expenses due to specialized hardware and fabrication.

9.2.2. Human-Centric Collaborative Robotics

Future cobots must ensure both safety and natural interaction in shared human–robot workspaces. Key implementation strategies include the following:

• Developing multi-modal perception systems (vision, force, voice) for real-time human–robot interaction [115].
• Incorporating affective computing for emotional recognition [116].
• Establishing trust calibration protocols across user demographics and cultural contexts through user studies and human trials.

Anticipated outcome: Safer, more intuitive cobots usable in healthcare, services, and manufacturing.

Obstacles: Ethical concerns, privacy in emotion-sensing, cultural variability in human–robot trust dynamics, and additional costs for regulatory compliance.

9.2.3. Sustainable Robotics Ecosystem

To align robotics with sustainability goals, recommended strategies include:

• Adopting lifecycle analysis (LCA) tools during robot design [117].
• Using biodegradable materials (e.g., soft robots made of gelatin-based or mycelium structures) [118].
• Integrating low-power electronics and energy harvesting systems [119].

Anticipated outcomes: Circular economy-compliant robotic platforms with reduced carbon footprints, minimal lifecycle waste, and longer off-grid deployment capacity.

Obstacles: Trade-offs in performance and durability of eco-materials; limited availability of recyclable actuators and batteries; cost for LCA adoption and the integration of biodegradable materials and energy harvesting modules.

9.2.4. Challenges and Future Prospects in University Robotics Education

University-level robotics education is undergoing rapid transformation, driven by technological advancements and the growing societal demand for automation and intelligent systems. Adaptive cognitive tutoring and AI-powered personalized learning platforms [120] promise to tailor content delivery according to individual learning styles and progress. Meanwhile, immersive technologies—such as virtual reality (VR), augmented reality (AR), and digital twins—facilitate scalable, hands-on experimentation, especially when physical robotics hardware is inaccessible [121]. Recent AI-assisted classroom behavior analysis has shown measurable improvements in engineering education engagement and outcomes [122], while studies on mixed reality (MR) device adoption have highlighted educators’ increasing acceptance of immersive tools as part of pedagogical innovation [123]. These innovations are supported by open source modular kits and 5G-enabled phygital labs, that integrate tangible components with real-time virtual simulations.

Despite these promising developments, several persistent challenges hinder the full realization of the potential of robotics education [124,125,126]:

• Resource Inequality: Many institutions, especially in low-income regions, lack access to modern robotics infrastructure, which limits experiential learning.
• Curricular Discontinuity: Robotics remains fragmented across disciplines (e.g., mechanical engineering, computer science), lacking cohesive, interdisciplinary curricula.
• Insufficient Industry Alignment: Educational programs often lag behind industrial trends, leaving graduates underprepared for evolving job market demands.
• Limited Undergraduate Research Exposure: Few opportunities exist for undergraduates to participate in real research, which stifles early critical thinking and innovation.

To address these gaps, the following forward-looking strategies are proposed:

• Co-designed Curricula: Partnering with industry to develop course content, capstone projects, and internships ensures alignment with emerging technological needs.
• Research-Integrated Teaching: Embedding active faculty research projects into undergraduate coursework fosters deeper learning and innovation.
• Remote and Cloud Robotics Labs: Digital twin systems and remote-access labs democratize access to high-quality robotics education across geographical and economic boundaries.
• Ecosystem Integration: Robotics programs should be embedded within innovation ecosystems such as incubators, smart factories, and research centers to enhance translational skills and entrepreneurship.

As robotics extends into sectors such as healthcare, sustainability, and domestic automation, academic programs must evolve to not only develop technical excellence but also cultivate ethical, inclusive, and socially responsive innovation. The future of robotics education lies in bridging research, industry, and pedagogy to equip learners with the technical and human dimensions of this transformative field.

9.2.5. Quantum-Enhanced Robotics

Quantum technologies hold promise for revolutionizing robot sensing and decision-making. The key implementation steps are as follows:

• Adopting quantum machine learning algorithms (e.g., QAOA) for optimization in multi-agent navigation or object recognition tasks [73,127].
• Deploying quantum sensors in field robotics for high-precision localization, particularly in GPS-denied environments [128].
• Testing hybrid quantum-classical systems in simulation environments before transitioning to physical robot control.

Anticipated outcome: Breakthroughs in real-time motion planning, ultra-precise indoor/outdoor navigation, and improved computational performance in complex tasks.

Obstacles: The immaturity of quantum hardware, steep learning curves, limited availability of deployable devices, high costs associated with expensive quantum devices and simulators, and need for specialized maintenance and operation.

9.2.6. Ethical and Regulatory Foundations

As robotics becomes increasingly integrated into society, there is a pressing need for robust ethical frameworks and policy guidelines. Addressing concerns related to privacy, security, and bias is crucial to ensure the responsible development and deployment of robotic technologies. To ensure responsible development, the following initiatives are crucial:

• Integration of robot ethics modules into core engineering curricula, promoting awareness of bias, safety, and fairness.
• Adoption of international safety and performance standards during design and certification, such as

  -

  ISO/TC 299, the International Organization for Standardization Technical Committee on Robotics.

  -

  IEEE IAB/SCSA), IEEE Industry Activities Board/Standards Coordinating Subcommittee on Autonomous Systems [129].

• Establishment of interdisciplinary ethics boards for large-scale robotics research projects, especially those involving AI or artificial general intelligence (AGI) [130].

Anticipated outcome: Trustworthy robotic systems that can be deployable in healthcare, defense, and public services while ensuring compliance with global safety and equity standards.

Obstacles: Evolving legal landscapes, cross-border inconsistencies, balancing innovation versus oversight, and cost of compliance testing.