1 citations · 2 across the 12 of their papers we have counts for
14 papers
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
Zemin Yang, Yaoyu He, Yiming Zhong +5
Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control. While recent…
From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
Yiming Zhong, Yaoyu He, Zemin Yang +5
Bridging high-level semantic understanding with low-level physical control remains a persistent challenge in embodied intelligence, stemming from the fundamental spatiotemporal sca…
ReMoGen: Real-time Human Interaction-to-Reaction Generation via Modular Learning from Diverse Data
Yaoqin Ye, Yiteng Xu, Qin Sun +3
Human behaviors in real-world environments are inherently interactive, with an individual's motion shaped by surrounding agents and the scene. Such capabilities are essential for a…
Driving Like Yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving
Xiaoru Dong, Ruiqin Li, Xiao Han +7
Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single average driving style, neglecting individual differences. Achie…
ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving
Yuhang Lu, Jiadong Tu, Yuexin Ma +1
End-to-end autonomous driving has emerged as a promising approach to unify perception, prediction, and planning within a single framework, reducing information loss and improving a…
HUMOF: Human Motion Forecasting in Interactive Social Scenes
Caiyi Sun, Yujing Sun, Xiao Han +5
Complex scenes present significant challenges for predicting human behaviour due to the abundance of interaction information, such as human-human and humanenvironment interactions.…
FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
Yiming Zhong, Yumeng Liu, Chuyang Xiao +7
Learning effective visuomotor policies for robotic manipulation is challenging, as it requires generating precise actions while maintaining computational efficiency. Existing metho…
EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignment
Yufei Zhu, Yiming Zhong, Zemin Yang +4
Dexterous robotic hands often struggle to generalize effectively in complex environments due to the limitations of models trained on low-diversity data. However, the real world pre…
FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments
Aoru Xue, Yiming Ren, Zining Song +3
We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments. Our system combines a single LiDAR with expandable…
Registration between Point Cloud Streams and Sequential Bounding Boxes via Gradient Descent
Xuesong Li, Xinge Zhu, Yuexin Ma +2
In this paper, we propose an algorithm for registering sequential bounding boxes with point cloud streams. Unlike popular point cloud registration techniques, the alignment of the…
Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving
Yuhang Lu, Yichen Yao, Jiadong Tu +3
Large Vision-Language Models (LVLMs) have recently garnered significant attention, with many efforts aimed at harnessing their general knowledge to enhance the interpretability and…
MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas
Feng Qiao, Zhexiao Xiong, Xinge Zhu +3
Omnidirectional depth estimation presents a significant challenge due to the inherent distortions in panoramic images. Despite notable advancements, the impact of projection method…
A Unified Framework for Human-centric Point Cloud Video Understanding
Yiteng Xu, Kecheng Ye, Xiao Han +3
Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds, further…
LaserHuman: Language-guided Scene-aware Human Motion Generation in Free Environment
Peishan Cong, Ziyi Wang, Zhiyang Dou +7
Language-guided scene-aware human motion generation has great significance for entertainment and robotics. In response to the limitations of existing datasets, we introduce LaserHu…