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This paper had been accepted by the 2025 IEEE International Conference on Engineering Reliable Autonomous Systems (ERAS). 

With the rapid development of deep reinforcement learning technology, it gradually demonstrates excellent potential and is becoming the most promising solution in the robotics. However, in the smart manufacturing domain, there is still not too much research involved in dynamic adaptive control mechanisms optimizing complex processes. This research advances the integration of Soft Actor-Critic (SAC) with digital twins for industrial robotics applications, providing a framework for enhanced adaptive real-time control for smart additive manufacturing processing. The system architecture combines Unity’s simulation environment with ROS2 for seamless digital twin synchronization, while leveraging transfer learning to efficiently adapt trained models across tasks. We demonstrate our methodology using a Viper X300s robot arm with the proposed hierarchical reward structure to address the common reinforcement learning challenges in two distinct control scenarios. The results show rapid policy convergence and robust task execution in both simulated and physical environments demonstrating the effectiveness of our approach. 

Many domains of AI and its effects are established, which mainly rely on their integration modeling cognition of human and AI agents, collecting and representing knowledge using them at the human level, and maintaining decision-making processes towards physical action eligible to and in cooperation with humans. Especially in human-robot interaction, many AI and robotics technologies are focused on human- robot cognitive modeling, from visual processing to symbolic reasoning and from reactive control to action recognition and learning, which will support human-multi-agent cooperative achieving tasks. However, the main challenge is efficiently combining human motivations and AI agents’ purposes in a sharing architecture and reaching a consensus in complex environments and missions. To fill this gap, this workshop brings together researchers from different communities inter- ested in multi-agent systems (MAS) and human-robot interaction (HRI) to explore potential approaches, future research directions, and domains in human-multi-agent cognitive fusion.