3 citations · 5 across the 7 of their papers we have counts for
7 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…
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…
KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation
Di Zhang, Chengbo Yuan, Chuan Wen +3
Collecting demonstrations enriched with fine-grained tactile information is critical for dexterous manipulation, particularly in contact-rich tasks that require precise force contr…
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…
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery
Xiao Zhang, Hai Zhang, Hongtu Zhou +4
Safety is one of the main challenges in applying reinforcement learning to realistic environmental tasks. To ensure safety during and after training process, existing methods tend…