8 accepted papers
We study the ability of Transformer models to learn sequences generated by Permuted Congruential Generators (PCGs), a widely used family of pseudo-random number generators (PRNGs). PCGs introduce substantial additional difficulty over linear congruential generators (LCGs) by applying a series of bit…
Unsupervised Anomaly Detection (UAD) is crucial for industrial quality control. Many existing embedding-based methods adopt a single-prototype assumption and learn, for example, a compact hypersphere to enclose all normal features. However, this strategy breaks down under intra-class variance caused
Weakly Supervised Video Anomaly Detection (WSVAD) aims to localize abnormal segments using only video-level labels during training.Although the paradigm significantly reduces annotation costs, the coarse-grained labels fail to precisely describe the full videos, resulting in the introduction of subs
Transformers excel at discovering patterns in sequential data, yet their fundamental limitations and learning mechanisms remain crucial topics of investigation. In this paper, we study the ability of Transformers to learn pseudo-random number sequences from linear congruential generators (LCGs), def…
Graph neural networks (GNNs) have been demonstrated to perform well in graph representation learning, but always lacking in generalization capability when tackling out-of-distribution (OOD) data. Graph invariant learning methods, backed by the invariance principle among defined multiple environments…
There is still a lack of null-space impedance control defined in Cartesian space that is suitable for multipoint contact and does not require knowledge of the force locations. To address this problem, this letter demonstrates a type of Cartesian null-space impedance control for the anthropomorphic m
Since the difference in angular velocity is used as the derivative of the orientation error in the classical impedance control, there is no longer a form of the second-order differential equation (SODE), and there is non-linearity in the classical impedance control, which limits applications. To add
In this work, we propose a framework to address the autonomous impedance regulation problem of robots in a class of constrained manipulation tasks. In this framework, a human arm endpoint stiffness model is used to extract the task stiffness geometry along the constrained trajectory, which is then e