3 citations · 4 across the 13 of their papers we have counts for
14 papers
GWM-VLA: Geometry-Aware Latent World Modeling for Vision-Language-Action Learning
Yanping Zhao, Hang Yu, Yiwei Wang +7
Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but often degrade under visual and environmental shifts. Latent world modeling offers a promisin…
TS-Mask VLA: 2D Temporal-Spatial Masking for Vision-Language-Action Model with Effective Bridging
Shengzhuo Yang, Ronghao Yu, Chuanjie Lv +5
Vision-language-action (VLA) models aim to understand natural-language instructions and visual observations, and to generate and execute corresponding actions as embodied agents. R…
Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning
Qingjun Wang, Hongtu Zhou, Hang Yu +5
Offline reinforcement learning (RL) faces a critical challenge of overestimating the value of out-of-distribution (OOD) actions. Existing methods mitigate this issue by penalizing…
Learning Native Continuation for Action Chunking Flow Policies
Yufeng Liu, Hang Yu, Juntu Zhao +9
Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at chunk boundaries. Real-Time Chunking…
Robust and Generalized Humanoid Motion Tracking
Yubiao Ma, Han Yu, Jiayin Xie +7
Learning a general humanoid whole-body controller is challenging because practical reference motions can exhibit noise and inconsistencies after being transferred to the robot doma…
Learning Geometrically-Grounded Amodal 3D Representations for View-Generalizable Robotic Manipulation
Di Zhang, Weicheng Duan, Dasen Gu +6
Real-world robotic manipulation requires visuomotor policies capable of robust 3D scene reasoning under varying camera viewpoints. While recent 3D-aware manipulation policies have…
ASTRO: Adaptive Stitching via Dynamics-Guided Trajectory Rollouts
Hang Yu, Di Zhang, Qiwei Du +5
Offline reinforcement learning (RL) enables agents to learn optimal policies from pre-collected datasets. However, datasets containing suboptimal and fragmented trajectories presen…
Point What You Mean: Visually Grounded Instruction Policy
Hang Yu, Juntu Zhao, Yufeng Liu +9
Vision-Language-Action (VLA) models align vision and language with embodied control, but their object referring ability remains limited when relying solely on text prompt, especial…
SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving
Peizheng Li, Zhenghao Zhang, David Holtz +6
End-to-end autonomous driving methods built on vision language models (VLMs) have undergone rapid development driven by their universal visual understanding and strong reasoning ca…
Open-world Hand-Object Interaction Video Generation Based on Structure and Contact-aware Representation
Haodong Yan, Hang Yu, Zhide Zhong +8
Generating realistic hand-object interactions (HOI) videos is a significant challenge due to the difficulty of modeling physical constraints (e.g., contact and occlusion between ha…
DexH2R: A Benchmark for Dynamic Dexterous Grasping in Human-to-Robot Handover
Youzhuo Wang, Jiayi Ye, Chuyang Xiao +6
Handover between a human and a dexterous robotic hand is a fundamental yet challenging task in human-robot collaboration. It requires handling dynamic environments and a wide varie…
MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs
Xueyao Wan, Hang Yu
Large Language Models (LLMs) suffer from hallucinations due to their static parametric knowledge. Retrieval-Augmented Generation (RAG) and GraphRAG mitigate this issue by incorpora…
Focus On What Matters: Separated Models For Visual-Based RL Generalization
Di Zhang, Bowen Lv, Hai Zhang +7
A primary challenge for visual-based Reinforcement Learning (RL) is to generalize effectively across unseen environments. Although previous studies have explored different auxiliar…
How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
Hai Zhang, Hang Yu, Junqiao Zhao +5
Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high couplin…