8 papers
Learning How the World Evolves: Extrapolative Video World Models via Latent Dynamics Reasoning
Haodong Li, Shaoteng Liu, Tianyu Wang +7
The world evolves following its dynamics, i.e., its laws of motion. However, leading video diffusion models largely fit the pixels without modeling how the pixels transit over time…
Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery
Yichang Liu, Tianyu Wang, Ziyi Ye +4
We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement. Motivated by th…
Say, Dream, and Act: Learning Video World Models for Instruction-Driven Robot Manipulation
Songen Gu, Yunuo Cai, Tianyu Wang +2
Robotic manipulation requires anticipating how the environment evolves in response to actions, yet most existing systems lack this predictive capability, often resulting in errors…
RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base
Kuanning Wang, Yuqian Fu, Tianyu Wang +4
Accurate 6D pose estimation is key for robotic manipulation, enabling precise object localization for tasks like grasping. We present RAG-6DPose, a retrieval-augmented approach tha…
You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping
Jingshun Huang, Haitao Lin, Tianyu Wang +3
This paper addresses the problem of category-level pose estimation for articulated objects in robotic manipulation tasks. Recent works have shown promising results in estimating pa…
CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image
Jingshun Huang, Haitao Lin, Tianyu Wang +3
This paper tackles category-level pose estimation of articulated objects in robotic manipulation tasks and introduces a new benchmark dataset. While recent methods estimate part po…