activity
20222026
most citedMasked Spiking Transformer

5 citations · 7 across the 19 of their papers we have counts for

collaborators

19 papers

cs.RO2026

GenHOI: Contact-Aware Humanoid-Object Interaction by Imitating Generated Videos without Task-Specific Training

Zhihai Bi, Qiang Zhang, Guoyang Zhao +8

Humanoid-Object Interaction (HOI) is a fundamental capability for humanoid robots, yet it remains challenging due to the tight coupling between dynamic balance and stable interacti…

cs.RO2026

GeoSem-WAM: Geometry- and Semantic-Aware World Action Models

Fulong Ma, Daojie Peng, Wenjun Yue +4

Recent World Action Models (WAMs) have demonstrated impressive capabilities in embodied decision-making. However, whether their effectiveness stems from explicit future imagination…

cs.RO2026

AttenA+: Rectifying Action Inequality in Robotic Foundation Models

Daojie Peng, Fulong Ma, Jiahang Cao +7

Existing robotic foundation models, while powerful, are predicated on an implicit assumption of temporal homogeneity: treating all actions as equally informative during optimizatio…

cs.LG2026

MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy

Jiaxu Wang, Junhao He, Jingkai Sun +5

Learning real-world dynamics from visual observations is crucial for various domains. A common strategy is to calibrate simulators by estimating physical parameters, yet accuracy i…

cs.RO2026

Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation

Yicheng Jiang, Jiaxu Wang, Junhao He +8

Current 3D-aware pretraining methods for embodied perception and manipulation are largely built on differentiable rendering frameworks, producing either fully implicit neural field…

cs.RO2025

Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots

Wei Cui, Haoyu Wang, Wenkang Qin +19

Humanoid robot technology is advancing rapidly, with manufacturers introducing diverse heterogeneous visual perception modules tailored to specific scenarios. Among various percept…

cs.RO2025

LOVON: Legged Open-Vocabulary Object Navigator

Daojie Peng, Jiahang Cao, Qiang Zhang +1

Object navigation in open-world environments remains a formidable and pervasive challenge for robotic systems, particularly when it comes to executing long-horizon tasks that requi…

cs.RO2025

Modality-Composable Diffusion Policy via Inference-Time Distribution-level Composition

Jiahang Cao, Qiang Zhang, Hanzhong Guo +3

Diffusion Policy (DP) has attracted significant attention as an effective method for policy representation due to its capacity to model multi-distribution dynamics. However, curren…

cs.CV2025

Exploring Typographic Visual Prompts Injection Threats in Cross-Modality Generation Models

Hao Cheng, Erjia Xiao, Yichi Wang +8

Current Cross-Modality Generation Models (GMs) demonstrate remarkable capabilities in various generative tasks. Given the ubiquity and information richness of vision modality input…

cs.RO2025

MapNav: A Novel Memory Representation via Annotated Semantic Maps for Vision-and-Language Navigation

Lingfeng Zhang, Xiaoshuai Hao, Qinwen Xu +7

Vision-and-language navigation (VLN) is a key task in Embodied AI, requiring agents to navigate diverse and unseen environments while following natural language instructions. Tradi…

cs.CV2025

Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras

Jingkai Sun, Qiang Zhang, Jiaxu Wang +2

Dynamic vision sensors (DVS) are bio-inspired devices that capture visual information in the form of asynchronous events, which encode changes in pixel intensity with high temporal…

cs.LG2025

A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning

Zhengpeng Xie, Yulong Zhang

Recently, empowered with the powerful capabilities of neural networks, reinforcement learning (RL) has successfully tackled numerous challenging tasks. However, while these models…

cs.LG2025

Representation Convergence: Mutual Distillation is Secretly a Form of Regularization

Zhengpeng Xie, Jiahang Cao, Changwei Wang +5

In this paper, we argue that mutual distillation between reinforcement learning policies serves as an implicit regularization, preventing them from overfitting to irrelevant featur…

cs.CV2024

Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Models

Hao Cheng, Erjia Xiao, Jiayan Yang +6

Current image generation models can effortlessly produce high-quality, highly realistic images, but this also increases the risk of misuse. In various Text-to-Image or Image-to-Ima…

cs.CV2024

Spiking Neural Network as Adaptive Event Stream Slicer

Jiahang Cao, Mingyuan Sun, Ziqing Wang +4

Event-based cameras are attracting significant interest as they provide rich edge information, high dynamic range, and high temporal resolution. Many state-of-the-art event-based a…

cs.NE2024★ 1 cited

Spiking Diffusion Models

Jiahang Cao, Hanzhong Guo, Ziqing Wang +4

Recent years have witnessed Spiking Neural Networks (SNNs) gaining attention for their ultra-low energy consumption and high biological plausibility compared with traditional Artif…

cs.CV2024

Transfer Attack for Bad and Good: Explain and Boost Adversarial Transferability across Multimodal Large Language Models

Hao Cheng, Erjia Xiao, Jiayan Yang +8

Multimodal Large Language Models (MLLMs) demonstrate exceptional performance in cross-modality interaction, yet they also suffer adversarial vulnerabilities. In particular, the tra…

cs.NE2023★ 1 cited

Spiking Denoising Diffusion Probabilistic Models

Jiahang Cao, Ziqing Wang, Hanzhong Guo +3

Spiking neural networks (SNNs) have ultra-low energy consumption and high biological plausibility due to their binary and bio-driven nature compared with artificial neural networks…

cs.NE2022★ 5 cited

Masked Spiking Transformer

Ziqing Wang, Yuetong Fang, Jiahang Cao +3

The combination of Spiking Neural Networks (SNNs) and Transformers has attracted significant attention due to their potential for high energy efficiency and high-performance nature…