Robot learning has attracted an ever-increasing attention by automating complex tasks, reducing errors, and increasing production speed and flexibility, which leads to significant advancements in manufacturing intelligence. However, its low training efficiency, limited real-time feedback, and challenges in adapting to untrained scenarios hinder its applications in smart manufacturing. Introducing a human role in the training loop, a practice known as human-in-the-loop (HITL) robot learning, can improve the performance of robots by leveraging human prior knowledge. Nonetheless, the exploration of HITL robot learning within the context of human-centric smart manufacturing remains in its infancy. This study provides a holistic literature review for understanding HITL robot learning within an industrial context from a human-centric perspective. A united structure is presented to encompass different aspects of human intelligence in HITL robot learning, highlighting perception, cognition, behavior, and notably, empathy. Then, the typical applications in manufacturing scenarios are analyzed to expand the research landscape for smart manufacturing. Finally, it introduces the empirical challenges and future directions for HITL robot learning in the next industrial revolution era.

Assistive robots for healthcare have witnessed a growing demand over the past decades. In this letter, we investigate the development of an optimization-based control framework with variable impedance for an assistive robot to perform ultrasound-guided scoliosis assessment. The conventional procedure for scoliosis assessment using ultrasound imaging typically requires a medical practitioner to slide an ultrasound probe along a patient’s back while maintaining a certain magnitude of the contact force. To automate such a procedure, we need to consider multiple objectives, such as contact force, position, orientation, energy, posture, etc. To coordinate different objectives, we propose to formulate the control framework as a quadratic programming problem with each objective weighted by a tunable task priority, subject to a set of equality and inequality constraints. As the procedure requires the robot to establish a constant contact force with the patient during scanning, we incorporate variable impedance regulation of the end-effector to enhance safety and stability during the physical human-robot interaction; The variable impedance gains are then retrieved by learning from medical expert’s demonstrations. The proposed methodology is evaluated with a robotic system performing autonomous scoliosis assessment with multiple human subjects involved. The effectiveness of our approach is verified by the coronal spinal images obtained with the robot.

Garment folding is a ubiquitous domestic task that is difficult to automate due to the highly deformable nature of fabrics. In this article, we propose a novel method of learning from demonstrations that enables robots to autonomously manipulate an assistive tool to fold garments. In contrast to traditional methods (that rely on low-level pixel features), our proposed solution uses a dense visual descriptor to encode the demonstration into a high-level hand-object graph (HoG) that allows to efficiently represent the interactions between the manipulated tool and robots. With that, we leverage graph neural network to autonomously learn the forward dynamics model from HoGs, then, given only a single demonstration, the imitation policy is optimized with a model predictive controller to accomplish the folding task. To validate the proposed approach, we conducted a detailed experimental study on a robotic platform instrumented with vision sensors and a custom-made end-effector that interacts with the folding board.

The automatic inspection of surface defects is an essential task for quality control in the computers, communications, and consumer electronics (3C) industry. Traditional inspection mechanisms (viz. line-scan sensors) have a limited field of view, thus, prompting the necessity for a multifaceted robotic inspection system capable of comprehensive scanning. Optimally selecting the robot’s viewpoints and planning a path is regarded as coverage path planning (CPP), a problem that enables inspecting the object’s complete surface while reducing the scanning time and avoiding misdetection of defects. In this paper, we present a new approach for robotic line scanners to detect surface defects of 3C free-form objects automatically. A two-stage region segmentation method defines the local scanning based on the random sample consensus (RANSAC) and K-means clustering to improve the inspection coverage. The proposed method also consists of an adaptive region-of-interest (ROI) algorithm to define the local scanning paths. Besides, a Particle Swarm Optimization (PSO)-based method is used for global inspection path generation to minimize the inspection time. The developed method is validated by simulation-based and experimental studies on various free-form workpieces, and its performance is compared with that of two state-of-the-art solutions. The reported results demonstrate the feasibility and effectiveness of our proposed method.

The recent emergence of Industry 5.0 underscores the need for increased autonomy in human–robot interaction (HRI), presenting both motivation and challenges in achieving resilient and energy-efficient production systems. To address this, in this article, we introduce a strategy for seamless collaboration between humans and robots in manufacturing and maintenance tasks. Our method enables smooth switching between temporary HRI (human-aware mode) and long-horizon automated manufacturing (fully automatic mode), effectively solving the human–robot coexistence problem. We develop a task progress monitor that decomposes complex tasks into robot-centric action sequences, further divided into three-phase subtasks. A trigger signal orchestrates mode switches based on detected human actions and their contribution to the task. In addition, we introduce a human agent coefficient matrix, computed using selected environmental features, to determine cut-points for reactive execution by each robot. To validate our approach, we conducted extensive experiments involving robotic manipulators performing representative manufacturing tasks in collaboration with humans. The results show promise for advancing HRI, offering pathways to enhancing sustainability within Industry 5.0. Our work lays the foundation for intelligent manufacturing processes in future societies, marking a pivotal step toward realizing the full potential of human–robot collaboration.

Different learning modes and mechanisms allow faster and better acquisition of skills as widely studied in humans and many animals. Specific neurons, called mirror neurons, are activated in the same way whether an action is performed or simply observed. This suggests that observing others performing movements allows to reinforce our motor abilities. This implies the presence of a biological mechanism that allows creating models of others' movements and linking them to the self-model for achieving mirroring. Inspired by such ability, we propose to build a map of movements executed by a teaching agent and mirror the agent's state to the robot's configuration space. Hence, in this study, a neural network is proposed to integrate a motor cortex-like differential map transforming motor plans from task-space to joint-space motor commands and a static map correlating joint-spaces of the robot and a teaching agent. The differential map is developed based on spiking neural networks while the static map is built as a self-organizing map. The developed neural network allows the robot to mirror the actions performed by a human teaching agent to its own joint-space and the reaching skill is refined by the complementary examples provided. Hence, experiments are conducted to quantify the improvement achieved thanks to the proposed learning approach and control scheme.