Physics-based simulation is essential for developing and evaluating robot manipulation policies, particularly in scenarios involving deformable objects and complex contact interactions. However, existing simulators often struggle to balance computational efficiency with numerical accuracy, especially when modeling deformable materials with frictional contact constraints. We introduce an efficient subspace representation for the Incremental Potential Contact (IPC) method, leveraging model reduction to decrease the number of degrees of freedom. Our approach decouples simulation complexity from the resolution of the input model by representing elasticity in a low-resolution subspace while maintaining collision constraints on an embedded high-resolution surface. Our barrier formulation ensures intersection-free trajectories and configurations regardless of material stiffness, time step size, or contact severity. We validate our simulator through quantitative experiments with a soft bubble gripper grasping and qualitative demonstrations of placing a plate on a dish rack. The results demonstrate our simulator's efficiency, physical accuracy, computational stability, and robust handling of frictional contact, making it well-suited for generating demonstration data and evaluating downstream robot training applications. More details and supplementary material are on the website: https://sites.google.com/view/embedded-ipc.

Grasping is fundamental to robotic manipulation, and recent advances in large-scale grasping datasets have provided essential training data and evaluation benchmarks, accelerating the development of learning-based methods for robust object grasping. However, most existing datasets exclude deformable bodies due to the lack of scalable, robust simulation pipelines, limiting the development of generalizable models for compliant grippers and soft manipulands. To address these challenges, we present GRIP, a General Robotic Incremental Potential contact simulation dataset for universal grasping. GRIP leverages an optimized Incremental Potential Contact (IPC)-based simulator for multi-environment data generation, achieving up to 48× speedup while ensuring efficient, intersection- and inversion-free simulations for compliant grippers and deformable objects. Our fully automated pipeline generates and evaluates diverse grasp interactions across 1,200 objects and 100,000 grasp poses, incorporating both soft and rigid grippers. The GRIP dataset enables applications such as neural grasp generation and stress field prediction. We release GRIP to advance research in robotic manipulation, soft-gripper control, and physics-driven simulation at: https://bell0o.github.io/GRIP/.

Social chatbots can provide users with specific social and emotional support, and intimate relationships represent an emerging area for exploration. This research describes the current state of self-disclosure in virtual romantic relationship dialogues. It examines the impact of self-disclosure with different emotions on the romantic attractiveness of virtual agents. Study 1 reveals that virtual romantic relationships are predominantly characterized by chat-based Platonic love, where individuals primarily establish these relationships to fulfill romantic needs. Study 2 highlights that perceived dialogue authenticity and anthropomorphism are vital factors attracting users to virtual agents. In contrast, the emotional category of self-disclosed personal stories in dialogues does not affect the romantic attractiveness of virtual agents. In the design of virtual romantic chatbots, more human-like conversational features, such as diverse emotional expressions, may be considered without necessarily maintaining a perpetually positive service demeanor.

This paper is concerned with the scaled formation control problem for multi-agent systems (MASs) over fixed and switching topologies. First, a modified resilient dynamic event-triggered (DET) mechanism involving an auxiliary dynamic variable (ADV) based on sampled data is proposed. In the proposed DET mechanism, a random variable obeying the Bernoulli distribution is introduced to express the idle and busy situations of communication networks. Meanwhile, the operation of absolute value is introduced into the triggering condition to effectively reduce the formation error. Second, a scaled formation control protocol with the proposed resilient DET mechanism is designed over fixed and switching topologies. The scaled formation error system is modeled as a time-varying delay system. Then, several sufficient stability criteria are derived by constructing appropriate Lyapunov–Krasovskii functionals (LKFs). A co-design algorithm based on the sparrow search algorithm (SSA) is presented to design the control gains and triggering parameters jointly. Finally, numerical simulations of multiple unmanned aerial vehicles (UAVs) are presented to validate the designed control method.

5G and artificial intelligence greatly promote the development of intelligent and connected vehicle (ICV). However, ICV opens more ports to the outside world, making it easy for hackers to intrude controller area network (CAN) and control ICV. Therefore, many researchers design intrusion detection systems (IDSs) to detect vehicle intrusion in real-time. In this paper, we propose a highly camouflaged attack method called the same origin method execution (SOME) attack. The intrusion messages of this attack have the same characteristics as normal messages and can bypass most existing IDSs. To detect this attack, we design a reliable IDS for ICV based on a generative adversarial network (GAN) called GVIDS. It takes CAN messages as the input sample and trains the IDS model to distinguish the legality of messages. Experiments on two real vehicles show that GVIDS can detect most existing attacks, including spoofing, bus-off, masquerade, and SOME attacks. The average detection accuracy of GVIDS is 96.64%, and the average running time of each detection is only 0.18 ms. In addition, the experiment also shows that the detection performance of GVIDS is not affected by the value of identifiers in CAN messages.

Biomedical images are usually corrupted by strong noise and intensity inhomogeneity simultaneously. Existing region-based active contour models (RACMs) easily fail when segmenting such images. In the frequency domain, we propose a generalized RACM that presents a new way to understand the essence of classical RACMs whose segmentation results are determined by a frequency filter to extract the proposed frequency boundary energy. Then, we introduce the difference of Gaussians as the optimal filter to exclude strong noise and intensity inhomogeneity effectively. We show superior performance of the model by comparing with six state-of-the-art methods on challenge biomedical images and segmenting an optical coherence tomography image sequence.