4 accepted papers
Depth completion, the task of reconstructing dense depth maps from sparse depth and RGB images, plays a critical role in 3D scene understanding. However, existing methods often struggle to recover high-frequency details, such as regions with fine structures or weak signals, since depth sensors may f…
Data-efficient domain adaptation with only a few labelled data is desired for many robotic applications, e.g., in grasping detection, the inference skill learned from a grasping dataset is not universal enough to directly apply on various other daily/industrial applications. This paper presents an a…
Environment understanding, object detection and recognition are crucial skills for robots operating in the real world. In this paper, we propose a Convolutional Neural Network with multi-task objectives: object detection and scene classification in one unified architecture. The proposed network reas…
Robot calibration is critical for industrial robot applications that require high accuracy. This paper presents a novel calibration method that utilizes Product-Of-Exponential (POE) and Gaussian Process (GP) regression to compensate for both geometric and non-geometric errors within the robot manipu