In this article, an adjustable autonomy framework is proposed for the human–robot collaboration (HRC) in which a robot uses a reinforcement learning mechanism guided by a human operator’s rewards in an initially unknown workspace. Within the proposed framework, the autonomy level of the robot is automatically adjusted in an HRC setting that is represented by a Markov decision process model. When the robot reaches higher performance levels, it can operate more autonomously in the sense that it needs less human operator intervention. A novel $Q$ -learning mechanism with an integrated $\epsilon$ -greedy approach is implemented for robot learning in order to capture the correct actions and robot’s mistakes as a basis for adjusting the robot’s autonomy level. The proposed HRC framework can adapt to changes in the workspace as well as changes in the human operator reward (scaling and shifting) mechanism, and can always adjust the autonomy level. The autonomy level of the robot is automatically lowered when the workspace changes to allow the robot to explore new actions in order to adapt to the new workspace. In addition, the human operator has the ability to reset/lower the autonomy level of the robot to enforce the robot to relearn the workspace if its performance is not satisfactory for the human operator. The developed algorithm is applied to a realistic HRC setting involving a humanoid robot, named Baxter. The experimental results are analyzed to assess the effectiveness of the proposed adjustable autonomy framework for different cases: for the case when the workspace does not change, then for the case when the robot autonomy level is reset/lowered by a human operator, and for the case when the workspace is changed by the introduction of new objects. The results confirm the capability of the developed framework to successfully adjust the autonomy level in response to changes in the human operator’s commands or the workspace.

In this study, a novel time-driven mathematical model for trust is developed considering human–multi-robot performance for a Human–Robot Collaboration (HRC) framework. For this purpose, a model is developed to quantify human performance considering the effects of physical and cognitive constraints and factors such as muscle fatigue and recovery, muscle isometric force, human (cognitive and physical) workload, workloads due to the robots’ mistakes, and task complexity. The performance of multi-robot in the HRC setting is modeled based upon the rate of task assignment and completion as well as the mistake probabilities of the individual robots. The human trust in HRC setting with single and multiple robots is modeled over different operation regions, namely unpredictable region, predictable region, dependable region, and faithful region. The relative performance difference between the human operator and the robot is used to analyze the effect on the human operator’s trust in robots’ operation. The developed model is simulated for a manufacturing workspace scenario considering different task complexities and involving multiple robots to complete shared tasks. The simulation results indicate that for a constant multi-robot performance in operation, the human operator’s trust in robots’ operation improves whenever the comparative performance of the robots improves with respect to the human operator performance. The impact of robot hypothetical learning capabilities on human trust in the same HRC setting is also analyzed. The results confirm that a hypothetical learning capability allows robots to reduce human workloads, which improves human performance. The simulation result analysis confirms that the human operator’s trust in the multi-robot operation increases faster with the improvement of the multi-robot performance when the robots have a hypothetical learning capability. An empirical study was conducted involving a human operator and two collaborator robots with two different performance levels in a software-based HRC setting. The experimental results closely followed the pattern of the developed mathematical models when capturing human trust and performance in terms of human–multi-robot collaboration.

Object detection represents a foundational capability in Artificial Intelligence (AI), enabling machines to interpret visual environments through precise object localization and classification. This comprehensive review chronicles the revolutionary evolution of the You Only Look Once (YOLO) framework from its inception to the state-of-the-art YOLOv12. Beginning with the limitations of classical approaches using handcrafted features, YOLO’s paradigm-shifting is documented transition to unified real-time detection via regression-based architectures. Methodically analyzing each major version (v1- v12), key innovations is detailed including multi-scale predictions (v2/v3), anchor-free designs (v8), programmable gradient information (v9), and attention-enhanced cross-scale fusion (v12). The review establishes how successive iterations systematically addressed critical challenges: reducing computational latency by 47× versus R-CNN variants, improving mAP by 32.7% on COCO benchmarks, and enabling deployment on edge devices. Beyond architectural analysis, comparative performance evaluations is presented across diverse applications—from autonomous driving to medical imaging—demonstrating YOLO’s unprecedented balance of speed (142 FPS) and accuracy (78.4% AP). The paper further examines emerging implementation trends, hardware optimizations, and domain-specific adaptations that cement YOLO’s position as the de facto framework for real-time vision systems. Our review analysis provides both technical and historical context for researchers and practitioners navigating the landscape of modern object detection.

This paper presents a comprehensive cross-era analysis of the algorithmic evolution of Large Language Models (LLMs) through four developmental epochs: Before Transformer (pre-2017), Transformer (post-2017), Instruction-tuned \& Open-source LLMs, and Multimodal Agents (2024-2025). A novel innovation pathway framework is introduced that traces causal relationships between architectural breakthroughs and emergent capabilities, addressing critical research gaps in three dimensions: (1) Cross-paradigm synthesis connecting statistical foundations to modern multimodal systems, (2) Causal innovation mapping demonstrating how architectural choices propagate through model generations, and (3) Cross-domain capability analysis quantifying transfer between representation learning, knowledge acquisition, behavioral alignment, and multimodal integration. This analysis reveals that LLM progression represents fundamental paradigm shifts rather than incremental improvements, with transformer architectures, human feedback mechanisms, and open-source ecosystems collectively enabling the transition from specialized NLP tools to general reasoning systems. We provide empirical evidence through case studies of capability emergence, quantify innovation impacts using performance metrics, and examine safety implications through recent jailbreak analysis and refusal mechanism studies. The contributions include: (a) a unified lifecycle synthesis with original analytical framework, (b) innovation trajectory mapping with causal pathway analysis, and (c) validated evolutionary principles for forecasting next-generation AI capabilities.