Natural and safe Human-to-Robot (H2R) object handover is a critical capability for effective Human–Robot Collaboration (HRC). However, learning a robust handover policy for this task is often hindered by the prohibitive cost of collecting physical robot demonstrations and the limitations of simplistic state representations that inadequately capture the complex dynamics of the interaction. To address these challenges, a two-stage learning framework is proposed that synthesizes substantially augmented, synthetically diverse handover demonstrations without requiring a physical robot and subsequently learns a handover policy from a rich 4D spatiotemporal flow. First, an offline, physical robot-free data-generation pipeline is introduced that produces augmented and diverse handover demonstrations, thereby eliminating the need for costly physical data collection. Second, a novel 4D spatiotemporal flow is defined as a comprehensive representation consisting of a skeletal kinematic flow that captures high-level motion dynamics and a geometric motion flow that characterizes fine-grained surface interactions. Finally, a diffusion-based policy conditioned on this spatiotemporal representation is developed to generate coherent and anticipatory robot actions. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines in task success, efficiency, and motion quality, thereby paving the way for safer and more intuitive collaborative robots.

Rapid advancements in digitalization and artificial intelligence (AI) have catalyzed the adoption of digital twin technologies in the construction sector, enabling real-time synchronization between virtual models and physical systems. Simultaneously, on-site robotic automation has shown promise for reducing physical workloads, enhancing productivity, and contributing to sustainability goals that are key values of Industry 5.0. However, current digital twin implementations rarely incorporate multirobot construction systems, often relying on single-robot approaches or purely offline simulations. This gap hinders the realization of truly integrated construction environments that combine sensing, data analytics, wireless communications, and multirobot coordination. In response, this article proposes ConstrucTwin, a digital twin-driven multirobot construction framework designed to support complex construction tasks in real-world settings. By combining a 5G communication estimation-involved architecture and a cross-level planning strategy, ConstrucTwin streamlines interactions between physical robots and their digital counterparts. Essential tasks such as motion and task-level planning, as well as remote human-in-the-loop (HIL) oversight, are orchestrated within a single unified architecture. Through case studies involving rebar cage and brick wall construction, we demonstrate how an integrated approach to vision-based servoing and multirobot coordination enhances execution speed, precision, and scalability. The results underscore the system’s potential to advance human-centric, resilient, and sustainable construction, thereby aligning with the broader vision of Industry 5.0.

Human-robot Collaboration (HRC) in unstructured environments increasingly underscores enabling robots to perceive, understand, and predict human motions to enhance its safety and efficiency, aligning with the principles of Industry 5.0. However, existing studies about human motion prediction primarily rely on deterministic methods, which struggle to capture and quantify the uncertainty in human movements, posing significant safety risks in collaborative environments. To this end, we propose a novel Fine-grained Motion Latent Diffusion (FineMLD) model tailored to generate precise and diverse human motion predictions, providing a crucial foundation for safe HRC. First, we introduce a scale-aware temporal motion latent space to capture spatio-temporal dependencies by using a time-aware human motion encoder and hierarchical motion disentanglement. The hierarchical motion disentanglement achieves a compact latent representation of the motion while enabling more precise motion decoding. Second, we perform mask-based fine-grained motion latent diffusion that incorporates trajectory-guided denoised prediction and motion mask-driven resampling modulation to refine motion predictions. The trajectory-guided denoised prediction integrates observed robot motion trajectories to guide human motion predictions, while the motion mask-driven resampling modulation aligns observed and predicted motions by using a motion mask, thereby improving the consistency and coherence of motion sequences. Finally, we validate the performance of FineMLD on a public dataset, demonstrating its state-of-the-art performance in prediction accuracy and diversity. Furthermore, we conduct physical experiments on a robotic platform to confirm its real-time applicability and robustness in a real-world laboratory setting, highlighting its potential for deployment in unstructured HRC tasks.

The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels.

The conventional automation approach has shown bottlenecks in the era of component assembly. What could be automated has been automated in some high tech industrial production, leaving manual work performed by humans. To achieve ergonomic working environments and better productivity, human–robot collabora tion has been adopted for this purpose through combining the strength, accuracy and repeatability of robots with adaptability, high-level cognition, and flexibility. A reliable human–robot collaborative setting should be supported by dynamically updated and precise models. For this purpose, the digital twin can realise the digital representation of physical collaborative settings through simulation modelling and data synchronisation but is limited by communication delay and constraints. This chapter will develop a digital twin-enabled approach to human–robot collaborative assembly. Within this approach, a sensor-driven 3D modelling of the physical devices of interest is developed to realise the physical-to-digital transformation of human–robot workcell, and a Wise-ShopFloor-based platform enabled by sensor data is used to develop a digital twin model of the physical human–robot workcell. Then, function blocks with embedded algorithms are used for assembly planning, decision making and robot control, and a time-ahead execution and planning approach is developed for reliable human–robot collaborative assembly. Finally, the performance of the developed system is demonstrated by a case study of a partial car engine assembly.

Human-robot collaboration (HRC) envisioned for future factories has been actively explored to facilitate higher overall productivity. The wide applications of HRC in multiple fields, such as manufacturing and production, have seen a series of milestones. In recent years, a shift towards intuitive and natural collaboration between humans and robots has been investigated and discussed for symbiotic scenarios and complex tasks. For this purpose, advancements of multimodality in HRC enable multimodal human-robot interactions and collaboration by utilising different communication channels such as auditory, vision, gestures, haptics, and even brain signals. In addition, understanding human behaviours in terms of intent and motion can be beneficial in achieving mutual human-robot assistance. Within an HRC setting, the digital twin of such a physical collaborative workcell offers a promising tool to implement sim-to-real transformation and on-demand support for real practice. Within the context, this study provides an overview of the past and current status of multimodal HRC and its applications and highlights future research directions.

Evaluating and training autonomous driving systems require diverse and scalable corner cases. However, most existing scene generation methods lack controllability, accuracy, and versatility, resulting in unsatisfactory generation results. Inspired by DragGAN in image generation, we propose DragTraffic, a generalized, interactive, and controllable traffic scene generation framework based on conditional diffusion. DragTraffic enables non-experts to generate a variety of realistic driving scenarios for different types of traffic agents through an adaptive mixture expert architecture. We employ a regression model to provide a general initial solution and a refinement process based on the conditional diffusion model to ensure diversity. User-customized context is introduced through cross-attention to ensure high controllability. Experiments on a real-world driving dataset show that DragTraffic outperforms existing methods in terms of authenticity, diversity, and freedom. Demo videos and code are available at https://chantsss.github.io/Dragtraffic/.