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Abstract 7: Adaptive Robot Coordination in Multi-Robot Systems Multi-robot systems require efficient coordination strategies to prevent conflicts and optimize task execution. This paper introduces an Adaptive Robot Coordination (ARC) framework that dynamically resolves inter-robot conflicts using a hybrid planning approach. By segmenting complex motion planning tasks into local subproblems, the proposed method ensures smooth navigation and optimal task allocation. Experimental results validate the framework’s effectiveness in enabling cooperative and autonomous multi-robot operations. Abstract 8: Adaptive Learning in Human-Robot Collaboration Human-robot collaboration demands adaptable robotic behavior in dynamic workspaces. This study explores reinforcement learning (RL) strategies that allow robots to refine their responses through experience. The proposed RL-based framework enables robots to optimize their actions based on human feedback, ensuring seamless interaction and enhanced efficiency. The study highlights applications in industrial automation and service robotics, demonstrating improved human-robot synergy. Abstract 9: Adaptive Neural Control for Robotic Manipulators Deep learning and neural networks provide powerful solutions for robotic control. This research introduces an adaptive neural control framework that dynamically adjusts to variations in payload, external forces, and environmental changes. By integrating deep learning techniques with real-time sensor feedback, the proposed method enhances robotic precision and stability. Simulation and experimental trials validate its effectiveness in improving adaptive manipulation tasks. Abstract 10: Bio-Inspired Navigation Strategies for Adaptive Robots Nature-inspired navigation methods offer robust solutions for autonomous robots. This study develops an adaptive robotic navigation system that mimics insect behavior, utilizing environmental cues and minimal memory-based learning. The bio-inspired approach enhances pathfinding efficiency, particularly in unknown and unstructured terrains. Experimental results demonstrate the system’s ability to navigate complex environments with minimal computational resources. Abstract 11: Reinforcement Learning for Adaptive Robotic Manipulation Reinforcement learning (RL) techniques empower robots to learn optimal grasping and manipulation strategies autonomously. This paper presents an RL-based policy optimization framework that enables robotic manipulators to refine their motor skills through trial-and-error learning. The study demonstrates improved dexterity and adaptability in handling diverse objects, making RL a viable solution for real-world robotic manipulation challenges. Abstract 12: Adaptive Motion Planning for Mobile Robots