Large-scale, diverse datasets are essential for training robust learning-based robotic manipulation models; however, their acquisition typically requires controlled environments and specialized hardware in research laboratories. This paper presents VRobotix, a virtual reality (VR)-based framework that enables cost-effective and scalable robotic dataset generation through immersive human-in-the-loop control within a physics-accurate robot simulation. By leveraging off-the-shelf VR headsets (e.g., Oculus Quest 3), VRobotix eliminates the need for physical robots while supporting a URDF-compatible, physics-based simulator that accommodates adaptable robotic platforms and egocentric control interfaces, including handheld controllers and body posture tracking. Benefiting from the physics-based simulation, a unique contribution of VRobotix is the replay module, which can regenerate synchronized multi-modal dataset (kinematic states, RGB-D streams) with multiple dataset formats based on the replayable trajectory, supporting various robotic applications. Additionally, an imitation learning module is developed to train control policies using the data collected by VRobotix. Experiments on three initial tasks—pushing, grasping, and stacking—demonstrate a high data collection success rate, averaging 92.0%. Furthermore, policies trained on just 50 trials achieve a 100% task success rate. VRobotix reduces infrastructure costs while generating ROS-compatible datasets, democratizing scalable robotic data acquisition.

Pouring fluids is a routine task for humans but challenging for high-DoF robots, particularly given fluid simulation’s computational demands while training policies. In this paper, we propose DexPour, a novel reinforcement learning method with hierarchical rewards and Approximated Proxy Abstraction (APA) method. APA efficiently approximates liquid behavior using a small set of spheres, reducing computational overhead. Meanwhile, our hierarchical reward framework breaks down the intricate pouring process into four distinct stages—approach, grasp, transport, and pour—providing fine-grained feedback and fostering stable policy learning. Extensive experiments demonstrate that DexPour achieves a 92% fluid transfer efficiency with a 70% cup fill and a 99% efficiency at 30% fill, highlighting its robust performance across varying liquid volumes. Ablation studies highlight the contribution of each component, confirming the necessity of detailed stage-wise guidance for complex dexterous manipulation. In addition, we compare DexPour with a full fluid simulation baseline, showing comparable pouring efficiency while reducing training time by 81.6%, demonstrating DexPour’s efficiency and practical viability for fluid manipulation tasks.

Mobile Virtual Reality (VR) is essential for achieving convenient and immersive human-computer interaction and realizing emerging applications such as Metaverse and spatial computing. However, existing VR technologies require two separate renderings of binocular images, thereby causing a significant bottleneck for mobile devices with limited computing and battery capacity. This paper proposes a new approach to optimizing mobile VR rendering called YORO. By utilizing the per-pixel attribute, YORO can generate binocular VR images from the monocular image through genuinely one rendering, saving half the computation over conventional approaches. Our experimental evaluation and detailed user study indicate that, YORO can save 27% power consumption on average and increase frame rate by 115.2%, while maintaining similar binocular image quality compared with state-of-the-art mobile VR rendering solutions. YORO is production-ready and has already been tested in real VR applications. The source code, demo video, prototype android app, video game engine plugins, and more are released anonymously at YORO-VR.github.io.

The rapid proliferation of Internet of Things (IoT) technologies has revolutionized connectivity but introduced significant cybersecurity vulnerabilities. This study investigates the disparity in IoT security awareness between Computer Science (CS) and non-CS students at the University of Colorado Denver, quantifying the educational gap and its implications. A survey of 300 students (150 CS, 150 non-CS) assessed knowledge on IoT principles, threats, and mitigation strategies. Statistical analysis, including chi-square tests, revealed significant disparities: CS students outperformed non-CS students, with mean scores of 9/10 and 4/10, respectively, highlighting a critical knowledge gap. Results indicate that educational background substantially influences IoT security awareness, underscoring the need for interdisciplinary IoT and cybersecurity education to promote a secure digital ecosystem.

Measuring the glucose concentration in liquids is crucial for ensuring the safety of products for individuals with diabetes. This process not only aids in diabetes management but also highlights the importance of taking additional precautions after a diabetes diagnosis. Currently, glucose test strips are widely used to measure the glucose concentration of liquids. It's safe, effective, easy to use, and a cheap alternative to other complex technology with the same use. However, it has sevral setbacks, such as its inability to accurately measure the glucose concentration of cold liquids (0°C - 10°C). This lack of variability can lead to more inconvenience for individuals than benefits. Therefore, in this paper, we propose the usage of millimeter-wave (mmWave) sensing as a contactless, versatile, and easy method to measure glucose levels in cold liquids accurately.

Playing is an essential way for preschoolers to learn. There are three types of games for preschoolers: functional play, constructive play, and symbolic play. However, existing works/games can only work for one specific type of children's play. Therefore, we designed and implemented an extensible children's game framework based on Augmented Reality building blocks. We implement an AR prototype from scratch that achieves up to 8 blocks with 81% detection rate and overhead of 21ms (46 FPS) on average.

Virtual Reality (VR) training is an emerging method, which is widely deployed in more and more applications. Compared with traditional physical training and video games-based training, VR training can not only provide a sense of realism and immersion similar to physical training but can also train at any time and place, saving time and money. However, due to some constraints like lacking reflections of the ambient environment, the realism and immersion of VR training are insufficient. Therefore, in this paper, we propose enhanced VR training which senses the ambient environment and reflects them as dynamic unexpected training tasks to solve the above problems.