2025· International Journal of Intelligent Automation & Robotics Engineering· Vol 8, pp. 01-15· 0 citations
TL;DR
Experimental results demonstrate that XORL enhances decision transparency, operator trust, safety awareness, and autonomous task performance while maintaining competitive learning efficiency, supporting the development of trustworthy and human-centric autonomous robotic systems.
Abstract
Autonomous robotic systems are increasingly deployed in industrial automation, healthcare, logistics, agriculture, defense, and intelligent transportation, where they must make complex decisions in dynamic environments. Reinforcement Learning (RL) enables robots to learn optimal actions through interaction with their environment, but most deep RL models function as black boxes, limiting transparency and trust in safety-critical applications. This paper proposes an Explainable Reinforcement Learning for Autonomous Robotic Decision Making (XORL) framework that integrates reinforcement learning with Explainable AI (XAI) to improve decision interpretability. The framework combines multimodal sensor data, policy optimization, confidence estimation, reward decomposition, policy visualization, and decision traceability to generate understandable explanations for robotic actions. It evaluates performance using metrics such as navigation success, obstacle avoidance, learning stability, computational efficiency, explanation consistency, and reliability. Experimental results demonstrate that XORL enhances decision transparency, operator trust, safety awareness, and autonomous task performance while maintaining competitive learning efficiency, supporting the development of trustworthy and human-centric autonomous robotic systems.
This work reviews pre-2019 XRL approaches, categorizing them into policy explanation, reward decomposition, model transparency, and post-hoc interpretability methods, and proposes a framework that combines interpretable policies, surrogate models, attention mechanisms, and visualization techniques to enhance transparency without significantly reducing performance.
Michael Anderson, David Thompson· International Journal of Art...· 0 citations
Autonomous robotic systems are transforming industries such as automation, healthcare, transportation, defense, and intelligent services. A major challenge in robotics is motion planning in dynamic and uncertain environments, where robots must navigate safely and efficiently. Traditional algorithms like Dijkstra’s, A*, Probabilistic Road Maps, and Rapidly Exploring Random Trees perform well in static environments but struggle with moving obstacles, sensor uncertainty, and real-time decision-making. Artificial Intelligence (AI) has improved robotic motion planning through adaptive learning, predictive decision-making, reinforcement learning, fuzzy logic, neural networks, and evolutionary optimization. These techniques enable robots to learn from their environment and optimize navigation over time. This paper reviews AI-based adaptive motion planning methods developed before 2019 and examines their role in path optimization, obstacle avoidance, localization, and decision-making. It also proposes a hybrid framework combining sensor fusion, environment mapping, fuzzy inference systems, and reinforcement learning for real-time path optimization. Simulation results demonstrate that AI-based adaptive planning achieves better navigation accuracy, obstacle avoidance, computational efficiency, and environmental adaptability compared to traditional methods.
Michael Anderson· International Journal of Int...· 0 citations
Intelligent robot navigation in dynamic environments remains one of the most challenging problems in autonomous robotics because navigation systems must continuously perceive environmental changes, predict moving obstacles, and generate safe trajectories while maintaining operational efficiency. Traditional navigation approaches, including graph-based path planning, rule-based obstacle avoidance, and probabilistic localization, often exhibit limited adaptability when environmental conditions change rapidly. Recent advances in artificial intelligence, particularly Deep Reinforcement Learning (DRL), have enabled autonomous robots to learn navigation policies directly from environmental interactions without relying exclusively on handcrafted rules. DRL integrates perception, decision-making, and continuous learning into a unified framework, making it particularly suitable for complex and uncertain environments such as warehouses, hospitals, manufacturing plants, urban streets, and disaster-response scenarios.
This research-review paper presents a comprehensive analysis of Deep Reinforcement Learning-based intelligent robot navigation with emphasis on dynamic obstacle avoidance, adaptive path planning, perception integration, reward optimization, and policy learning. The paper synthesizes contemporary studies related to artificial intelligence, semantic decision intelligence, cyber-physical systems, cloud intelligence, autonomous optimization, and intelligent infrastructure to establish a multidisciplinary understanding of modern robotic navigation. Particular attention is devoted to semantic AI-enabled decision intelligence, which enhances contextual understanding during navigation and improves policy robustness in continuously evolving environments (Goyal, 2025).
A conceptual DRL navigation framework is proposed comprising environmental perception, state representation, policy optimization, experience replay, reward engineering, semantic reasoning, and adaptive trajectory generation. The framework demonstrates how semantic knowledge, sensor fusion, and reinforcement learning cooperate to produce robust navigation strategies under uncertainty. Furthermore, the paper evaluates challenges involving sparse rewards, safety constraints, computational complexity, sim-to-real transfer, multi-agent coordination, and real-time deployment.
Hiroshi Tanaka· European International Journ...· 0 citations
Artificial Intelligence (AI), Machine Learning (ML), and autonomous intelligent systems are transforming predictive decision-making across industries such as manufacturing, healthcare, finance, transportation, cybersecurity, and smart cities. Traditional centralized machine learning models often struggle to adapt to dynamic and uncertain environments. This paper proposes an Agent-Based Machine Learning Framework for Autonomous Predictive Decision Systems (ABML-APDS) that integrates distributed intelligent agents, collaborative learning, reinforcement learning, predictive analytics, and explainable AI into a unified architecture. The framework enables autonomous agents to collect data, engineer features, exchange knowledge, optimize predictions, and continuously improve decision-making with minimal human intervention. It incorporates explainable decision mechanisms to enhance transparency, trust, and interpretability while supporting supervised, unsupervised, deep, and reinforcement learning models. Continuous learning and decentralized agent collaboration improve adaptability, scalability, fault tolerance, computational efficiency, and real-time responsiveness. The proposed framework provides an intelligent and scalable foundation for next-generation autonomous predictive systems supporting Industry 5.0, cyber-physical systems, IoT, smart manufacturing, precision healthcare, and AI-driven digital transformation.
Grace Ndlovu, Samuel Johnson· International Journal of Mac...· 0 citations
Intelligent robotics has transformed industrial automation, healthcare, logistics, autonomous transportation, agriculture, and service applications by enabling robots to perform complex tasks with minimal human intervention. However, conventional robotic systems rely on offline supervised learning models trained on static datasets, limiting their ability to adapt to dynamic environments, sensor variations, changing tasks, and unforeseen conditions. Frequent retraining increases computational cost, downtime, and catastrophic forgetting. Continual learning addresses these limitations by enabling robots to acquire new knowledge while preserving previously learned skills through adaptive memory management, knowledge consolidation, reinforcement learning, and dynamic neural architectures. This paper proposes a comprehensive continual learning framework that integrates adaptive knowledge representation, experience replay, task-aware optimization, reinforcement learning-based policy refinement, and dynamic parameter consolidation. The framework supports long-term knowledge retention, rapid adaptation, and stable sequential learning while mitigating catastrophic forgetting. Mathematical formulations for continual optimization, adaptive loss minimization, knowledge retention, and policy adaptation are also presented. Experimental evaluation using metrics such as adaptation accuracy, task completion rate, learning efficiency, knowledge retention, inference latency, computational overhead, energy consumption, and catastrophic forgetting demonstrates superior performance compared with conventional deep learning and reinforcement learning approaches. Furthermore, the framework supports scalable cloud-edge robotic ecosystems for collaborative learning and knowledge sharing, making it well suited for Industry 5.0 manufacturing, autonomous vehicles, intelligent warehouses, healthcare robotics, and smart city applications. Overall, the proposed framework establishes continual learning as a fundamental approach for achieving lifelong, adaptive, and intelligent robotic systems.
John McCarthy, M. Minsky· International Journal of Int...· 0 citations
This work employs Inductive Logic Programming (ILP) to extract symbolic representations of RL policies and define a novel set of explainability metrics, including activation rate, feature coverage, syntactic distance and semantic distance, which provide crucial insights for the transfer and generalization of action-specific policies.
