Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control

• URL: http://arxiv.org/abs/2501.18016v2
• Date: Mon, 09 Jun 2025 23:33:56 GMT
• Title: Digital Twin Synchronization: Bridging the Sim-RL Agent to a Real-Time Robotic Additive Manufacturing Control
• Authors: Matsive Ali, Sandesh Giri, Sen Liu, Qin Yang,
• Abstract summary: This research advances the integration of Soft Actor-Critic with digital twins for industrial robotics applications.<n>The system architecture combines Unity's simulation environment with ROS2 for seamless digital twin synchronization.<n>Results show rapid policy convergence and robust task execution in both simulated and physical environments.
• Score: 2.5709786140685633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Reducing Latency in LLM-Based Natural Language Commands Processing for Robot Navigation [0.2516577526761521]
  This study explores the integration of the ChatGPT natural language model with the Robot Operating System 2 (ROS 2) to mitigate interaction latency.<n>We present an architecture that integrates these technologies without requiring a transport platform.<n> Experimental results demonstrate that this integration improves execution speed, usability, and accessibility of the human-robot interaction.
  arXiv  Detail & Related papers  (2025-05-29T21:16:14Z)
• Action Flow Matching for Continual Robot Learning [57.698553219660376]
  Continual learning in robotics seeks systems that can constantly adapt to changing environments and tasks. We introduce a generative framework leveraging flow matching for online robot dynamics model alignment. We find that by transforming the actions themselves rather than exploring with a misaligned model, the robot collects informative data more efficiently.
  arXiv  Detail & Related papers  (2025-04-25T16:26:15Z)
• Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation [50.34179054785646]
  We present Taccel, a high-performance simulation platform that integrates IPC and ABD to model robots, tactile sensors, and objects with both accuracy and unprecedented speed.<n>Taccel provides precise physics simulation and realistic tactile signals while supporting flexible robot-sensor configurations through user-friendly APIs.<n>These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development.
  arXiv  Detail & Related papers  (2025-04-17T12:57:11Z)
• CTSAC: Curriculum-Based Transformer Soft Actor-Critic for Goal-Oriented Robot Exploration [3.435901586870572]
  Reinforcement Learning (RL) is becoming a promising approach in the field of autonomous robotic exploration. Current RL-based exploration algorithms often face limited environmental reasoning capabilities, slow convergence rates, and substantial challenges in Sim-To-Real transfer. We propose a Curriculum Learning-based Transformer Reinforcement Learning Algorithm (CTSAC) aimed at improving both exploration efficiency and transfer performance.
  arXiv  Detail & Related papers  (2025-03-18T13:44:29Z)
• Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks [2.6092377907704254]
  This paper proposes a two-phase system to improve robot learning.<n>The first phase captures real-time data from operators using a robot arm linked with a virtual simulator via ROS-Sharp.<n>In the second phase, this feedback is converted into robotic motion instructions, using a generative approach to incorporate force feedback into the learning process.
  arXiv  Detail & Related papers  (2025-01-24T22:01:23Z)
• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  We introduce a novel framework for learning world models.<n>By providing a scalable and robust framework, we pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• RObotic MAnipulation Network (ROMAN) $\unicode{x2013}$ Hybrid Hierarchical Learning for Solving Complex Sequential Tasks [70.69063219750952]
  We present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN) ROMAN achieves task versatility and robust failure recovery by integrating behavioural cloning, imitation learning, and reinforcement learning. Experimental results show that by orchestrating and activating these specialised manipulation experts, ROMAN generates correct sequential activations for accomplishing long sequences of sophisticated manipulation tasks.
  arXiv  Detail & Related papers  (2023-06-30T20:35:22Z)
• DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control [0.0]
  Delayed Markov decision processes fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. We introduce a disturbance-augmented Markov decision process in delayed settings as a novel representation to incorporate disturbance estimation in training on-policy reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2023-06-15T10:11:38Z)
• Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics [11.946807588018595]
  This paper presents a unified model-based reinforcement learning framework that bridges active exploration and uncertainty-aware deployment. The two opposing tasks of exploration and deployment are optimized through state-of-the-art sampling-based MPC. We conduct experiments on both autonomous vehicles and wheeled robots, showing promising results for both exploration and deployment.
  arXiv  Detail & Related papers  (2023-05-20T17:20:12Z)
• Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning [24.223788665601678]
  Two xArm6 robots solve the U-shape assembly task with a success rate of above90% in simulation, and 50% on real hardware without any additional real-world fine-tuning. Our results present a significant step forward for bi-arm capability on real hardware, and we hope our system can inspire future research on deep RL and Sim2Real transfer bi-manualpolicies.
  arXiv  Detail & Related papers  (2023-03-27T01:25:24Z)
• Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments [0.3867363075280544]
  In reinforcement learning (RL) research, simulations enable benchmarks between algorithms. In this paper, we introduce Karolos, a framework developed for robotic applications. The code is open source and published on GitHub with the aim of promoting research of RL applications in robotics.
  arXiv  Detail & Related papers  (2022-12-01T23:14:02Z)
• Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration [68.94506047556412]
  We propose to leverage a sequential bias to learn control policies for complex robotic tasks using a single demonstration. We show that DCIL-II can solve with unprecedented sample efficiency some challenging simulated tasks such as humanoid locomotion and stand-up.
  arXiv  Detail & Related papers  (2022-11-09T10:28:40Z)
• Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control [46.81433026280051]
  We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
  arXiv  Detail & Related papers  (2022-10-23T00:45:05Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning [121.9708998627352]
  Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off. This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
  arXiv  Detail & Related papers  (2022-04-15T08:12:15Z)
• Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space [7.116986445066885]
  Reinforcement Learning has led to promising results on a range of challenging decision-making tasks. Fast human-like adaptive control methods can optimize complex robotic interactions, yet fail to integrate multimodal feedback needed for unstructured tasks. We propose to factor the learning problem in a hierarchical learning and adaption architecture to get the best of both worlds.
  arXiv  Detail & Related papers  (2021-10-19T12:09:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.