7 papers
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…
OCRA: Object-Centric Learning with 3D and Tactile Priors for Human-to-Robot Action Transfer
Kuanning Wang, Ke Fan, Yuqian Fu +6
We present OCRA, an Object-Centric framework for video-based human-to-Robot Action transfer that learns directly from human demonstration videos to enable robust manipulation. Obje…
TP-MDDN: Task-Preferenced Multi-Demand-Driven Navigation with Autonomous Decision-Making
Shanshan Li, Da Huang, Yu He +3
In daily life, people often move through spaces to find objects that meet their needs, posing a key challenge in embodied AI. Traditional Demand-Driven Navigation (DDN) handles one…
TriVLA: A Triple-System-Based Unified Vision-Language-Action Model with Episodic World Modeling for General Robot Control
Zhenyang Liu, Yongchong Gu, Sixiao Zheng +3
Recent advances in vision-language models (VLMs) have enabled robots to follow open-ended instructions and demonstrate impressive commonsense reasoning. However, current vision-lan…
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…