Underwater object detection suffers from limited long-range dependency modeling, fine-grained feature representation, and noise suppression, resulting in blurred boundaries, frequent missed detections, and reduced robustness. To address these challenges, we propose the Mamba-Driven Feature Pyramid Decoding framework, which employs a parallel Feature Pyramid Network and Path Aggregation Network collaborative pathway to enhance semantic and geometric features. A lightweight Mamba Block models long-range dependencies, while an Adaptive Sparse Self-Attention module highlights discriminative targets and suppresses noise. Together, these components improve feature representation and robustness. Experiments on two publicly available underwater datasets demonstrate that MFPD significantly outperforms existing methods, validating its effectiveness in complex underwater environments. The code is publicly available at:https://github.com/YitengGuo/MFPD

The ability of autonomous subtask generation is important for robots to effectively cope with unforeseen situations during indoor search and rescue missions. While prior work mainly focused on improving individual low-level skills of the rescue robot, this paper proposes AutoExpand: a high-level framework that takes advantage of the extensive knowledge and reasoning abilities inherent in large language models (LLM) to understand human instructions and environmental situation. Through tight coupling LLM with behavior tree, our method enables the robot to autonomously generate reactive context-aware operational subtasks on-site without human intervention or additional training. A series of real-world experiments demonstrate that AutoExpand can effectively generate appropriate tasks for search and rescue missions, leading to a search scope increased by 34.45% when compared with traditional methods. The sample code is available at https://github.com/nubot-nudt/AutoExpand.

The evolution from motion capture and teleoperation to robot skill learning has emerged as a hotspot and critical pathway for advancing embodied intelligence. However, existing systems still face a persistent gap in simultaneously achieving four objectives: accurate tracking of full upper limb movements over extended durations (Accuracy), ergonomic adaptation to human biomechanics (Comfort), versatile data collection (e.g., force data) and compatibility with humanoid robots (Versatility), and lightweight design for outdoor daily use (Convenience). We present a wearable exoskeleton system, incorporating user-friendly immersive teleoperation and multi-modal sensing collection to bridge this gap. Due to the features of a novel shoulder mechanism with synchronized linkage and timing belt transmission, this system can adapt well to compound shoulder movements and replicate 100% coverage of natural upper limb motion ranges. Weighing 5.2 kg, NuExo supports backpack-type use and can be conveniently applied in daily outdoor scenarios. Furthermore, we develop a unified intuitive teleoperation framework and a comprehensive data collection system integrating multi-modal sensing for various humanoid robots. Experiments across distinct humanoid platforms and different users validate our exoskeleton’s superiority in motion range and flexibility, while confirming its stability in data collection and teleoperation accuracy in dynamic scenarios. The videos are available on our project website at https://nubot-nuexo.github.io/

Abstract In a digital society, the rapid development of computer science and the Internet has greatly facilitated image applications. However, one of the public network also brings risks to both image tampering and privacy exposure. Image authentication is the most important approaches to verify image integrity and authenticity. However, it has been challenging for image authentication to address both issues of tampering detection and privacy protection. One aspect, image authentication requires image contents not be changed to detect tampering. The other, privacy protection needs to remove sensitive information from images, and as a result, the contents should be changed. In this paper, we propose a practical image authentication scheme constructed from chameleon hashes combined with ordinary digital signatures to make tradeoff between tampering detection and privacy protection. Our scheme allows legitimate users to modify contents of authenticated images with a privacy-aware purpose (for example, cover some sensitive areas with mosaics) according to specific rules and verify the authenticity without interaction with the original authenticator. The security of our scheme is guaranteed by the security of the underlying cryptographic primitives. Experiment results show that our scheme is efficient and practical. We believe that our work will facilitate image applications where both authentication and privacy protection are desirable.