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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.
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This content will become publicly available on November 11, 2026
Digital twin-enabled real-time control for robot arm-based manufacturing via reinforcement learning
Recent advancements in Digital Twin (DT) technology have opened new avenues for smart manufacturing. These systems increasingly depend on adaptive control mechanisms to optimize complex processes and reduce production wastage. This research presents an innovative approach that integrates Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm with DT technology with Robot Operating System 2 (ROS2) to enable real-time adaptive control in robotic manufacturing. Our experimental setup consists of a ViperX 300 S robot arm, in which two distinct Tasks: (1) static target reaching and (2) dynamic target following were implemented for simulating adaptive control of manufacturing process. The innovative system architecture combines Unity game engine’s simulation environment with ROS2 for seamless and robust DT synchronization. We implemented a hierarchical reward structure to address common RL challenges, including local minima avoidance, convergence acceleration, and training stability, while leveraging transfer learning to efficiently adapt trained behavior models across tasks. Experimental results demonstrate rapid policy convergence and robust task execution, with performance metrics including cumulative reward, value loss, policy loss, and entropy validating the effectiveness of the approach. To the best of our knowledge, this is the first study to integrate Unity with ROS2-based DT for real-time synchronization and adaptive physical robot control using RL. Unlike prior works limited to offline or low-frequency simulations, our framework achieves stable 20 ms joint-level synchronization, enabling deployment of learned behaviors directly to physical robotic systems through virtual platform. This work advances the integration of RL with realistic DT framework for industrial and manufacturing robotics applications, providing a framework for enhanced adaptive real-time control in smart additive manufacturing (AM) processes.
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- Award ID(s):
- 2348013
- PAR ID:
- 10647078
- Publisher / Repository:
- Springer Netherlands
- Date Published:
- Journal Name:
- Journal of Intelligent Manufacturing
- ISSN:
- 0956-5515
- Subject(s) / Keyword(s):
- Control, Robotics, Automation, Industrial Automation, Motor Control, Rehabilitation Robotics, Robotic Engineering, Robotics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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