7 papers
RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization
Songming Liu, Bangguo Li, Kai Ma +5
Vision-Language-Action (VLA) models hold promise for generalist robotics but currently struggle with data scarcity, architectural inefficiencies, and the inability to generalize ac…
Motus: A Unified Latent Action World Model
Hongzhe Bi, Hengkai Tan, Shenghao Xie +13
While a general embodied agent must function as a unified system, current methods are built on isolated models for understanding, world modeling, and control. This fragmentation pr…
Vidarc: Embodied Video Diffusion Model for Closed-loop Control
Yao Feng, Chendong Xiang, Xinyi Mao +7
Robotic arm manipulation in data-scarce settings is a highly challenging task due to the complex embodiment dynamics and diverse contexts. Recent video-based approaches have shown…
H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation
Hongzhe Bi, Lingxuan Wu, Tianwei Lin +4
Imitation learning for robotic manipulation faces a fundamental challenge: the scarcity of large-scale, high-quality robot demonstration data. Recent robotic foundation models ofte…
RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function
Yunrui Yu, Kafeng Wang, Hang Su +1
Despite their widespread success, deep neural networks remain critically vulnerable to adversarial attacks, posing significant risks in safety-sensitive applications. This paper in…
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
Yunrui Yu, Hang Su, Cheng-zhong Xu +2
Gradient-based adversarial attacks using the Cross-Entropy (CE) loss often suffer from overestimation due to relative errors in gradient computation induced by floating-point arith…