7 citations · 9 across the 16 of their papers we have counts for
16 papers
Robotic Scene Cloning:Advancing Zero-Shot Robotic Scene Adaptation in Manipulation via Visual Prompt Editing
Binyuan Huang, Yuqing Wen, Yucheng Zhao +5
Modern robots can perform a wide range of simple tasks and adapt to diverse scenarios in the well-trained environment. However, deploying pre-trained robot models in real-world use…
ManiAgent: An Agentic Framework for General Robotic Manipulation
Yi Yang, Kefan Gu, Yuqing Wen +4
While Vision-Language-Action (VLA) models have demonstrated impressive capabilities in robotic manipulation, their performance in complex reasoning and long-horizon task planning i…
IntentionVLA: Generalizable and Efficient Embodied Intention Reasoning for Human-Robot Interaction
Yandu Chen, Kefan Gu, Yuqing Wen +3
Vision-Language-Action (VLA) models leverage pretrained vision-language models (VLMs) to couple perception with robotic control, offering a promising path toward general-purpose em…
LLaDA-VLA: Vision Language Diffusion Action Models
Yuqing Wen, Hebei Li, Kefan Gu +3
The rapid progress of auto-regressive vision-language models (VLMs) has inspired growing interest in vision-language-action models (VLA) for robotic manipulation. Recently, masked…
Hita: Holistic Tokenizer for Autoregressive Image Generation
Anlin Zheng, Haochen Wang, Yucheng Zhao +4
Vanilla autoregressive image generation models generate visual tokens step-by-step, limiting their ability to capture holistic relationships among token sequences. Moreover, becaus…
ROSA: Harnessing Robot States for Vision-Language and Action Alignment
Yuqing Wen, Kefan Gu, Haoxuan Liu +4
Vision-Language-Action (VLA) models have recently made significant advance in multi-task, end-to-end robotic control, due to the strong generalization capabilities of Vision-Langua…
PADriver: Towards Personalized Autonomous Driving
Genghua Kou, Fan Jia, Weixin Mao +7
In this paper, we propose PADriver, a novel closed-loop framework for personalized autonomous driving (PAD). Built upon Multi-modal Large Language Model (MLLM), PADriver takes stre…
Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness
Haochen Wang, Yucheng Zhao, Tiancai Wang +3
The rapid development of Large Multimodal Models (LMMs) for 2D images and videos has spurred efforts to adapt these models for interpreting 3D scenes. However, the absence of large…
Reconstructive Visual Instruction Tuning
Haochen Wang, Anlin Zheng, Yucheng Zhao +4
This paper introduces reconstructive visual instruction tuning (ROSS), a family of Large Multimodal Models (LMMs) that exploit vision-centric supervision signals. In contrast to co…
Panacea+: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated video training data. In this paper, we propose Panacea+, a powerful and universally applicable framework…
Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?
Yifan Bai, Dongming Wu, Yingfei Liu +8
Rapid advancements in Autonomous Driving (AD) tasks turned a significant shift toward end-to-end fashion, particularly in the utilization of vision-language models (VLMs) that inte…
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
Binyuan Huang, Yuqing Wen, Yucheng Zhao +9
Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data…
Stream Query Denoising for Vectorized HD Map Construction
Shuo Wang, Fan Jia, Yingfei Liu +6
To enhance perception performance in complex and extensive scenarios within the realm of autonomous driving, there has been a noteworthy focus on temporal modeling, with a particul…
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving
Yuqing Wen, Yucheng Zhao, Yingfei Liu +7
The field of autonomous driving increasingly demands high-quality annotated training data. In this paper, we propose Panacea, an innovative approach to generate panoramic and contr…
ADriver-I: A General World Model for Autonomous Driving
Fan Jia, Weixin Mao, Yingfei Liu +5
Typically, autonomous driving adopts a modular design, which divides the full stack into perception, prediction, planning and control parts. Though interpretable, such modular desi…
VLM-Eval: A General Evaluation on Video Large Language Models
Shuailin Li, Yuang Zhang, Yucheng Zhao +4
Despite the rapid development of video Large Language Models (LLMs), a comprehensive evaluation is still absent. In this paper, we introduce a unified evaluation that encompasses m…