18 citations · 18 across the 8 of their papers we have counts for
8 papers
Lucida: Parse, Generate, and Place for Composable Real-to-Sim Scene Modeling
Minghan Qin, Yuang Wang, Xiuyu Yang +6
Composable scene modeling aims to recover a real indoor scene as complete, editable object assets arranged as observed, giving robot simulation and embodied AI a simulation-ready r…
Utonia: Toward One Encoder for All Point Clouds
Yujia Zhang, Xiaoyang Wu, Yunhan Yang +6
We dream of a future where point clouds from all domains can come together to shape a single model that benefits them all. Toward this goal, we present Utonia, a first step toward…
Any3D-VLA: Enhancing VLA Robustness via Diverse Point Clouds
Xianzhe Fan, Shengliang Deng, Xiaoyang Wu +7
Existing Vision-Language-Action (VLA) models typically take 2D images as visual input, which limits their spatial understanding in complex scenes. How can we incorporate 3D informa…
End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection
Yu Cui, Yujian Zhang, Lina Tao +3
Achieving human-like dexterous manipulation remains a major challenge for general-purpose robots. While Vision-Language-Action (VLA) models show potential in learning skills from d…
OmniPerson: Unified Identity-Preserving Pedestrian Generation
Changxiao Ma, Chao Yuan, Xincheng Shi +6
Person re-identification (ReID) suffers from a lack of large-scale high-quality training data due to challenges in data privacy and annotation costs. While previous approaches have…
Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations
Yujia Zhang, Xiaoyang Wu, Yixing Lao +4
Humans learn abstract concepts through multisensory synergy, and once formed, such representations can often be recalled from a single modality. Inspired by this principle, we intr…
NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance
Wenzhe Cai, Jiaqi Peng, Yuqiang Yang +6
Learning to navigate in dynamic and complex open-world environments is a critical yet challenging capability for autonomous robots. Existing approaches often rely on cascaded modul…
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model
Zhenhua Xu, Yujia Zhang, Enze Xie +5
Multimodal large language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-text…