27 citations · 87 across the 9 of their papers we have counts for
9 papers
End-to-end Multi-Instance Robotic Reaching from Monocular Vision
Zheyu Zhuang, Xin Yu, Robert Mahony
Multi-instance scenes are especially challenging for end-to-end visuomotor (image-to-control) learning algorithms. "Pipeline" visual servo control algorithms use separate detection…
GoferBot: A Visual Guided Human-Robot Collaborative Assembly System
Zheyu Zhuang, Yizhak Ben-Shabat, Jiahao Zhang +2
The current transformation towards smart manufacturing has led to a growing demand for human-robot collaboration (HRC) in the manufacturing process. Perceiving and understanding th…
Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception
Ziwei Wang, Liyuan Pan, Yonhon Ng +2
Stereo camera systems play an important role in robotics applications to perceive the 3D world. However, conventional cameras have drawbacks such as low dynamic range, motion blur…
Iterative Optimisation with an Innovation CNN for Pose Refinement
Gerard Kennedy, Zheyu Zhuang, Xin Yu +1
Object pose estimation from a single RGB image is a challenging problem due to variable lighting conditions and viewpoint changes. The most accurate pose estimation networks implem…
LyRN (Lyapunov Reaching Network): A Real-Time Closed Loop approach from Monocular Vision
Zheyu Zhuang, Xin Yu, Robert Mahony
We propose a closed-loop, multi-instance control algorithm for visually guided reaching based on novel learning principles. A control Lyapunov function methodology is used to desig…
6DoF Object Pose Estimation via Differentiable Proxy Voting Loss
Xin Yu, Zheyu Zhuang, Piotr Koniusz +1
Estimating a 6DOF object pose from a single image is very challenging due to occlusions or textureless appearances. Vector-field based keypoint voting has demonstrated its effectiv…
Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge
S. Wade-McCue, N. Kelly-Boxall, M. McTaggart +19
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the…
Semantic Segmentation from Limited Training Data
A. Milan, T. Pham, K. Vijay +22
We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transpa…
Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge
D. Morrison, A. W. Tow, M. McTaggart +19
The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. Th…