activity
20172026
most citedTowards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation

41 citations · 78 across the 18 of their papers we have counts for

collaborators

24 papers

cs.CV2026

HRDiT: Training-Free High-Resolution Image Generation with Off-the-Shelf Diffusion Transformer Models

Yu Xue, Haoxuan Qu, Zhuoling Li +5

Training-free text-to-high-resolution image generation has recently attracted growing research attention. However, existing studies on this task primarily focus on adapting off-the…

cs.AI2025

Transforming Future Data Center Operations and Management via Physical AI

Zhiwei Cao, Minghao Li, Feng Lin +4

Data centers (DCs) as mission-critical infrastructures are pivotal in powering the growth of artificial intelligence (AI) and the digital economy. The evolution from Internet DC to…

cs.RO2025

Unified Locomotion Transformer with Simultaneous Sim-to-Real Transfer for Quadrupeds

Dikai Liu, Tianwei Zhang, Jianxiong Yin +1

Quadrupeds have gained rapid advancement in their capability of traversing across complex terrains. The adoption of deep Reinforcement Learning (RL), transformers and various knowl…

cs.MM2024

Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning

Meng Shen, Yake Wei, Jianxiong Yin +3

Training multimodal models requires a large amount of labeled data. Active learning (AL) aim to reduce labeling costs. Most AL methods employ warm-start approaches, which rely on s…

cs.RO2024

Masked Sensory-Temporal Attention for Sensor Generalization in Quadruped Locomotion

Dikai Liu, Tianwei Zhang, Jianxiong Yin +1

With the rising focus on quadrupeds, a generalized policy capable of handling different robot models and sensor inputs becomes highly beneficial. Although several methods have been…

cs.CV2023★ 2 cited

Learning Gabor Texture Features for Fine-Grained Recognition

Lanyun Zhu, Tianrun Chen, Jianxiong Yin +2

Extracting and using class-discriminative features is critical for fine-grained recognition. Existing works have demonstrated the possibility of applying deep CNNs to exploit featu…