Computer-generated holography (CGH) is a promising technology for next-generation displays. However, generating high-speed, high-quality holographic video requires both high frame rate display and efficient computation, but is constrained by two key limitations: (i) Learning-based models often produce over-smoothed phases with narrow angular spectra, causing severe color crosstalk in high frame rate full-color displays such as depth-division multiplexing and thus resulting in a trade-off between frame rate and color fidelity. (ii) Existing frame-by-frame optimization methods typically optimize frames independently, neglecting spatial-temporal correlations between consecutive frames and leading to computationally inefficient solutions. To overcome these challenges, in this paper, we propose a novel high-speed full-color video CGH generation scheme. First, we introduce Spectrum-Guided Depth Division Multiplexing (SGDDM), which optimizes phase distributions via frequency modulation, enabling high-fidelity full-color display at high frame rates. Second, we present HoloMamba, a lightweight asymmetric Mamba-Unet architecture that explicitly models spatial-temporal correlations across video sequences to enhance reconstruction quality and computational efficiency. Extensive simulated and real-world experiments demonstrate that SGDDM achieves high-fidelity full-color display without compromise in frame rate, while HoloMamba generates FHD (1080p) full-color holographic video at over 260 FPS, more than 2.6 times faster than the prior state-of-the-art Divide-Conquer-and-Merge Strategy.

Video Snapshot Compressive Imaging (SCI) provides an elegant solution for recording fast-dynamics motion through optical compression and computational reconstruction. However, existing video reconstruction algorithms are computationally intensive and typically require substantial computing and storage resources to large areas of static background with minimal information, leading to significant resource waste. To this end, this paper proposes a motion-aware reconstruction paradigm, in which moving objects are separated from the static background to identify Regions Of Interest (ROI) in the measurement domain and then video reconstruction is confined to ROI to save reconstruction costs. Specifically, a Motion Decomposition Network (MoDeNet) is proposed to get ROI by deep unfolding of Half-Quadratic Splitting (HQS). Subsequently, high-performance reconstruction network is used to recover ROI videos. Extensive experiments demonstrate that the proposed paradigm reduces the reconstruction costs of video SCI effectively and efficiently, and the proposed MoDeNet outperforms previous decomposition algorithms in terms of accuracy and speed.

Computer-generated holography (CGH) has gained wide attention with deep learning-based algorithms. However, due to its nonlinear and ill-posed nature, challenges remain in achieving accurate and stable reconstruction. Specifically, (i) the widely used end-to-end networks treat the reconstruction model as a black box, ignoring underlying physical relationships, which reduces interpretability and flexibility. (ii) CNN-based CGH algorithms have limited receptive fields, hindering their ability to capture long-range dependencies and global context. (iii) Angular spectrum method (ASM)-based models are constrained to finite near-fields. In this paper, we propose a Deep Unfolding Network (DUN) that decomposes gradient descent into two modules: an adaptive bandwidth-preserving model (ABPM) and a phase-domain complex-valued denoiser (PCD), providing more flexibility. ABPM allows for wider working distances compared to ASM-based methods. At the same time, PCD leverages its complex-valued deformable self-attention module to capture global features and enhance performance, achieving a PSNR over 35 dB. Experiments on simulated and real data show state-of-the-art results. Code is available at https://github.com/HannahZhang1926/Complex-Valued-Deformable-Transformer-for-CGH.

Learning from Demonstration (LfD) is a framework that allows lay users to easily program robots. However, the efficiency of robot learning and the robot’s ability to generalize to task variations hinge upon the quality and quantity of the provided demonstrations. Our objective is to guide human teachers to provide more effective demonstrations, thus facilitating efficient robot learning. To achieve this, we propose to use a measure of uncertainty, namely task-related information entropy , as a criterion for suggesting informative demonstration examples to human teachers to improve their teaching skills. This approach seeks to minimize the requisite number of demonstrations by enhancing their distribution throughout the workspace. In a conducted experiment \((N = 24)\) , an augmented reality (AR)-based guidance system was employed to train novice users to produce additional demonstrations from areas with the highest entropy within the workspace. These novice users were trained for a few trials to teach the robot a generalizable task using a limited number of demonstrations. Subsequently, the users’ performance after training was assessed first on the same task (retention) and then on a new task (transfer) without guidance. The results indicate a substantial improvement in robot learning efficiency from the teacher’s demonstrations, with an improvement of up to 198% observed on the novel task. Furthermore, the proposed approach was compared to a state-of-the-art heuristic rule and found to improve robot learning efficiency by 210% compared to the heuristic rule. The scripts used in this article are available on GitHub .

We provide an experimental evaluation of a wearable augmented reality (AR) system we have developed for human-robot teams working on tasks requiring collaboration in shared physical workspace. Recent advances in AR technology have facilitated the development of more intuitive user interfaces for many human-robot interaction applications. While it has been anticipated that AR can provide a more intuitive interface to robot assistants helping human workers in various manufacturing scenarios, existing studies in robotics have been largely limited to teleoperation and programming. Industry 5.0 envisions cooperation between human and robot working in teams. Indeed, there exist many industrial tasks that can benefit from human-robot collaboration. A prime example is high-value composite manufacturing. Working with our industry partner towards this example application, we evaluated our AR interface design for shared physical workspace collaboration in human-robot teams. We conducted a multi-dimensional analysis of our interface using established metrics. Results from our user study (n = 26) show that, subjectively, the AR interface feels more novel and a standard joystick interface feels more dependable to users. However, the AR interface was found to reduce physical demand and task completion time, while increasing robot utilization. Furthermore, user’s freedom of choice to collaborate with the robot may also affect the perceived usability of the system.