This study proposes a framework that integrates Transfer Learning (TL) into Multi-Agent Reinforcement Learning (MARL) for real-time, energy-efficient thermal control of the heating stage in thermoforming processes, while concurrently minimizing energy consumption and adapting to varying manufacturing conditions. To enable a scalable framework under these conditions, a Fully Connected Neural Network-based Multi-Agent Proximal Policy Optimization (FCNN-MAPPO) architecture is developed using a multi-objective reward function for concurrently minimizing control error, thermal energy consumption, and instability, while preserving temporal dynamics through state augmentation. The resulting Multi-Agent Transfer Reinforcement Learning (MATRL) framework combines direct parameter transfer for within-scenario learning with experience sharing for cross-scenario adaptation, enabling faster convergence and improved generalization. Results show that under high convective heat transfer conditions, MATRL can reduce training time by 29.3%, decrease energy consumption by 28.1%, and improve average error by 2.02 °C compared to baseline MARL (i.e., without TL). Under elevated ambient temperature conditions, energy usage and settling time were reduced by 42.6% and 41.7%, respectively. Under synchronous multi-parameter variations (extreme conditions of convective heat transfer, sheet conductivity, and ambient temperature), MATRL maintained energy efficiency within 0.5% of nominal conditions while reducing temperature dispersion by 47%, demonstrating robust multidimensional adaptability without retraining. Statistical validation across multiple runs with random seeds showed stable performance, with a coefficient of variation of 7.3% and no divergence.

Safe Reinforcement Learning (Safe RL) is one of the prevalently studied subcategories of trial-and-error-based methods with the intention to be deployed on real-world systems. In safe RL, the goal is to maximize reward performance while minimizing constraints, often achieved by setting bounds on constraint functions and utilizing the Lagrangian method. However, deploying Lagrangian-based safe RL in real-world scenarios is challenging due to the necessity of threshold fine-tuning, as imprecise adjustments may lead to suboptimal policy convergence. To mitigate this challenge, we propose a unified Lagrangian-based model-free architecture called Meta Soft Actor-Critic Lagrangian (Meta SAC-Lag). Meta SAC-Lag uses meta-gradient optimization to automatically update the safety-related hyperparameters. The proposed method is designed to address safe exploration and threshold adjustment with minimal hyperparameter tuning requirement. In our pipeline, the inner parameters are updated through the conventional formulation and the hyperparameters are adjusted using the meta-objectives which are defined based on the updated parameters. Our results show that the agent can reliably adjust the safety performance due to the relatively fast convergence rate of the safety threshold. We evaluate the performance of Meta SAC-Lag in five simulated environments against Lagrangian baselines, and the results demonstrate its capability to create synergy between parameters, yielding better or competitive results. Furthermore, we conduct a real-world experiment involving a robotic arm tasked with pouring coffee into a cup without spillage. Meta SAC-Lag is successfully trained to execute the task, while minimizing effort constraints. The success of Meta SAC-Lag in performing the experiment is intended to be a step toward practical deployment of safe RL algorithms to learn the control process of safety-critical real-world systems without explicit engineering.

The rise of automation has provided an opportunity to achieve higher efficiency in manufacturing processes, yet it often compromises the flexibility required to promptly respond to evolving market needs and meet the demand for customization. Human-robot collaboration attempts to tackle these challenges by combining the strength and precision of machines with human ingenuity and perceptual understanding. In this paper, we conceptualize and propose an implementation framework for an autonomous, machine learning-based manipulator that incorporates human-in-the-loop principles and leverages Extended Reality (XR) to facilitate intuitive communication and programming between humans and robots. Furthermore, the conceptual framework foresees human involvement directly in the robot learning process, resulting in higher adaptability and task generalization. The paper highlights key technologies enabling the proposed framework, emphasizing the importance of developing the digital ecosystem as a whole. Additionally, we review the existent implementation approaches of XR in human-robot collaboration, showcasing diverse perspectives and methodologies. The challenges and future outlooks are discussed, delving into the major obstacles and potential research avenues of XR for more natural human-robot interaction and integration in the industrial landscape.

Reinforcement learning (RL) for motion planning of multi-degree-of-freedom robots still suffers from low efficiency in terms of slow training speed and poor generalizability. In this article, we propose a novel RL-based robot motion planning framework that uses implicit behavior cloning (IBC) and dynamic movement primitive (DMP) to improve the training speed and generalizability of an off-policy RL agent. IBC utilizes human demonstration data to leverage the training speed of RL, and DMP serves as a heuristic model that transfers motion planning into a simpler planning space. To support this, we also create a human demonstration dataset using a pick-and-place experiment that can be used for similar studies. Comparison studies reveal the advantage of the proposed method over the conventional RL agents with faster training speed and higher scores. A real-robot experiment indicates the applicability of the proposed method to a simple assembly task. Our work provides a novel perspective on using motion primitives and human demonstration to leverage the performance of RL for robot applications.