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This paper introduces a human-centered approach to integrating Generative AI into Cyber-Physical Manufacturing (CPM) systems, addressing challenges such as resource constraints and extensive model sizes. The authors develop a hierarchical deep reinforcement learning algorithm that minimizes average task completion latency by optimizing AIGC service associations and resource allocations at smart machines and edge servers. Simulation results demonstrate that their method significantly outperforms existing benchmarks, leading to enhanced efficiency and productivity in human-machine interactions.
Reducing average task completion latency in Cyber-Physical Manufacturing by optimizing Generative AI service provision could redefine human-machine collaboration in industrial settings.
Integrating Generative Artificial Intelligence (GAI) into Industry 5.0 presents transformative potential for Cyber-Physical Manufacturing (CPM) systems, enabling the human-machine interactive operations with higher degree of efficiency and productivity. However, deploying Artificial Intelligence Generated Content (AIGC) services in the CPM systems is fraught with challenges, such as resource constraints, extensive model sizes, and stochastic task generation. Therefore, it is essential to provision AIGC services through a co-design of fine-tuning service association, fine-tuned model transmission, and resource allocation. Notably, Industry 5.0鈥檚 emphasis on human-centricity allows humans to experience a more smooth operational interaction with smart machines (SMs), which necessitates shortening the completion latency of AIGC tasks. In this paper, we propose a human-centered AIGC service provision scheme for jointly designing the AIGC service associations of fine-tuning and model transmission at the SMs and edge servers (ESs), as well as the bandwidth and transmit power allocations at the ESs. We target the complete workflow particularly with model fine-tuning, model transmission, and inference execution. On this basis, we formulate the AIGC service provision problem for average task completion latency minimization, and develop a hierarchical deep reinforcement learning algorithm to solve it. Specifically, the discrete action policy of AIGC service association is learned via Dueling Double Deep Q-Network (D3QN) in the first layer, to guide the training of the second layer that learns the continuous action policy of resource allocation by soft actor-critic (SAC). Moreover, to improve the training stability and accelerate convergence, curriculum learning is adopted during the training phase to progressively update the neural networks of both D3QN and SAC. Simulation results show that the proposed scheme outperforms benchmark schemes, achieving a significant reduction in average task completion latency.
