Optimization of vision-based deep reinforcement learning frameworks to improve robotic manipulation tasks by means of sophisticated greedy osprey optimizer

Unstructured robot manipulation remains a major challenge owing to variations in object geometry, lighting conditions, and task requirements, which restrict the flexibility of conventional control techniques. Although deep reinforcement learning has emerged as a promising method for facilitating end-to-end policy learning from sensory inputs, the existing frameworks are usually sample-inefficient, struggle to generalize across robot embodiments, and are vulnerable to visual noise. This research introduces a novel vision-based deep reinforcement learning framework that synergistically combines Vision Transformers for powerful perceptual encoding with a policy network actively optimized by the Advanced Greedy Osprey Optimizer (AGOO), a metaheuristic algorithm. The model is validated on the Open X-Embodiment Dataset, which is used to train policies that can generalize to a wide range of manipulation skills. The proposed system employs curriculum learning, decomposition of skills, and uncertainty-sensitive policies in order to improve the stability and safety of training. Experimental findings show that the framework is more successful in tasks, less prone to generalization breakpoints, and more efficient in computation than the state-of-the-art baselines. Combining state-of-the-art metaheuristic optimization with vision-based DRL can lead to more adaptive and trustworthy robotic systems, thereby contributing to progress in autonomous robotics, such as industrial, medical, and service robots.