5 citations · 17 across the 11 of their papers we have counts for
14 papers
EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ
Yunsong Zhou, Xuekun Jiang, Haiqi Shen +11
Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as tasks unfold. Scaling collection…
Data-Free Continual Learning for World Action Model with Rectified Generative Replay
Kaipeng Fang, Hangxu Liu, Chaojian Shi +5
World action models (WAMs) built on pretrained video generation models adapt quickly to new robotic tasks, but they forget previously learned tasks under sequential fine-tuning. Ex…
GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models
Shuai Wang, Yaxin Feng, Xuekun Jiang +12
Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states a…
SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation
Mu Huang, Hui Wang, Kerui Ren +5
Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and…
Decoupled Diffusion Sparks Adaptive Scene Generation
Yunsong Zhou, Naisheng Ye, William Ljungbergh +6
Controllable scene generation could reduce the cost of diverse data collection substantially for autonomous driving. Prior works formulate the traffic layout generation as predicti…
SimGen: Simulator-conditioned Driving Scene Generation
Yunsong Zhou, Michael Simon, Zhenghao Peng +4
Controllable synthetic data generation can substantially lower the annotation cost of training data. Prior works use diffusion models to generate driving images conditioned on the…
Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension
Quan Liu, Hongzi Zhu, Zhenxi Wang +3
Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario, which is vital for driving safety rela…
Embodied Understanding of Driving Scenarios
Yunsong Zhou, Linyan Huang, Qingwen Bu +7
Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded…
Density-invariant Features for Distant Point Cloud Registration
Quan Liu, Hongzi Zhu, Yunsong Zhou +3
Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of collaborative autonomous vehicles, and yet is challenging due to small overlapping area…
APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction
Quan Liu, Yunsong Zhou, Hongzi Zhu +2
For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extr…
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation
Yunsong Zhou, Quan Liu, Hongzi Zhu +3
Monocular 3D object detection (Mono3D) in mobile settings (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Due to the near-far disparity phenomenon o…
MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer
Yunsong Zhou, Hongzi Zhu, Quan Liu +2
Mobile monocular 3D object detection (Mono3D) (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Existing transformer-based offline Mono3D models adopt…
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
Yunsong Zhou, Yuan He, Hongzi Zhu +3
Monocular 3D object detection is an important task in autonomous driving. It can be easily intractable where there exists ego-car pose change w.r.t. ground plane. This is common du…
On Geometric Structure of Activation Spaces in Neural Networks
Yuting Jia, Haiwen Wang, Shuo Shao +3
In this paper, we investigate the geometric structure of activation spaces of fully connected layers in neural networks and then show applications of this study. We propose an effi…