Mobile robot inspection is a representative application of the Internet of Robotic Things (IoRT), where robots serve as mobile IoT sensing terminals for equipment monitoring and data acquisition. While embodied intelligence has greatly advanced locomotion, navigation, and perception, the critical deployment phase remains underexplored. Conventional inspection deployment relies on on-site manual configuration and repeated debugging, making it labor-intensive, costly, and potentially unsafe. To address these issues, this paper presents the design and implementation of a virtual deployment system for mobile robot inspection. The system constructs a high-fidelity digital twin of the physical site, migrating the entire deployment process into a virtual environment. The paper details the system architecture and workflow and analyzes key challenges with corresponding solutions. The proposed system enables scalable and cost-efficient IoRT inspection in industrial environments.

Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, i.e., periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment. Project page:https://ljf1113.github.io/IPAD_VAD.

The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccurate body motions, lack of hand gestures and fine- grained textual descriptions. To better perceive and generate human-human interactions, we propose Inter-X, a currently largest human-human interaction dataset with accurate body movements and diverse interaction patterns, together with detailed hand gestures. The dataset includes