collaborators

23 papers

cs.AI2026

Agentic RAG-VLM: Affordance-Aware Retrieval-Augmented Generation with Self-Reflective Planning for Robotic Grasping

Tao Chen, Lizheng Liu, Jiaxu Wang +4

Generalizable robotic grasping in cluttered environments is essential for deploying manipulators in unstructured human spaces, yet existing VLM-based methods rely on visual similar…

cs.RO2026

-WM: A Unified Video-Action World Model for Robotic Manipulation

Pengfei Zhou, Shengcong Chen, Di Chen +17

Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execution. We present -World…

cs.CV2026

MVISTA-4D: View-Consistent 4D World Model with Test-Time Action Inference for Robotic Manipulation

Jiaxu Wang, Yicheng Jiang, Tianlun He +8

World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely image-based forecasting or reasoni…

cs.LG2026

MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy

Jiaxu Wang, Junhao He, Jingkai Sun +5

Learning real-world dynamics from visual observations is crucial for various domains. A common strategy is to calibrate simulators by estimating physical parameters, yet accuracy i…

cs.RO2026

Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation

Yicheng Jiang, Jiaxu Wang, Junhao He +8

Current 3D-aware pretraining methods for embodied perception and manipulation are largely built on differentiable rendering frameworks, producing either fully implicit neural field…

cs.RO2026

Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition

Jiahang Cao, Yize Huang, Hanzhong Guo +15

Diffusion-based models for robotic control, including vision-language-action (VLA) and vision-action (VA) policies, have demonstrated significant capabilities. Yet their advancemen…