Semantic communication (SC) systems face the challenge of completing generation and comprehension tasks simultaneously under high data compression. To address this issue, this paper proposes a You Only Transmit Once (YOTO) SC system, which achieves efficient performance by synergistically integrating images and texts. For generation tasks, YOTO integrates a multiple granularity visual encoder, a digital modulation autoencoder, and a conditional probability-based visual decoder. These parts collectively ensure high-fidelity image reconstruction and accurate object detection. Meanwhile, YOTO utilizes the Source Feature Selection (SFS) and Channel Feature Selection (CFS) module for joint compression of source and channel coding data. To achieve a globally optimal compression, the Libra module dynamically adjusts feature retention ratios between CFS and SFS. Regarding comprehension tasks, YOTO eliminates the need for additional text data transmission by simply replacing the visual decoder with a Multimodal Language Model that incorporates the visual causality and bridging module. This architecture effectively converts demodulated visual data into descriptive captions while enabling emotion-aware text generation through prompt engineering. Extensive simulation experiments demonstrate the superior performance of YOTO in diverse channel conditions. In generation tasks, the YOTO system achieves PSNR values exceeding 30 dB both in the AWGN and Rayleigh fading channels when the SNR is 6 dB. While in comprehension tasks, it attains sentence similarity scores above 0.83 at 10 dB SNR. YOTO system effectively resolves cross-modal semantic disparities, thereby paving the way for future developments of SC systems that can balance generation and comprehension tasks.

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning, details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain.

Steganography facilitates secure communication by concealing messages within digital media, with video emerging as a preferred cover medium due to its inherent high-capacity data embedding capability. Despite its potential, limitations in terms of its robustness and embedding effectiveness still hinder its application in the mainstream. To overcome the aforementioned limitations, we introduce Diffusion-Driven Video Steganography, a novel three-stage framework that synergistically integrates QR code encoding with advanced video processing techniques. In the first stage, QR code is introduced to convert secret information into images, providing assurance for the correct recovery. The second stage employs diffusion models enhanced with attention mechanisms to dynamically adapt embedding strategies, enabling seamless integration of data into video content while preserving visual fidelity. Finally, the invertibility of the Denoising Diffusion Implicit Models (DDIM) is utilized to accurately reconstruct the hidden information from the steganographic video. Extensive experiments conducted on multiple benchmark datasets validate the effectiveness of our framework, which consistently surpasses conventional video steganography methods in terms of embedding capacity, imperceptibility, and robustness to real-world perturbations.