用数字孪生+强化学习，让机器人3D打印实时自适应控制
Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
- 数字孪生融合SAC算法，实现仿真与真实机器人的同步控制
- 在模拟和真实环境中均实现快速策略收敛与稳定执行
- 适合智能制造、机器人控制方向的研究者参考
随着深度强化学习技术的快速发展，其在机器人领域展现出巨大潜力，成为最有前景的解决方案之一。然而，在智能制造领域，针对复杂工艺的动态自适应控制机制研究仍较为有限。本研究将Soft Actor-Critic（SAC）算法与数字孪生结合，为智能增材制造过程提供增强型自适应实时控制框架。系统架构采用Unity仿真环境与ROS2协同，实现无缝数字孪生同步，并利用迁移学习高效适配不同任务的训练模型。通过Viper X300s机械臂配合分层奖励结构，在两种不同的控制场景中验证了方法的有效性。结果表明，该方法在模拟与物理环境中均实现了快速策略收敛和鲁棒的任务执行，证明了其优越性。
原文摘要 · Abstract (English)
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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