Object Learning for 6D Pose Estimation and Grasping from RGB-D Videos of In-hand Manipulation.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021
Unsupervised Domain Adaptation Through Inter-Modal Rotation for RGB-D Object Recognition.
IEEE Robotics Autom. Lett., 2020
DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping.
Frontiers Robotics AI, 2020
Learn, detect, and grasp objects in real-world settings.
Elektrotech. Informationstechnik, 2020
Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images.
Proceedings of the Computer Vision - ECCV 2020, 2020
Multi-Task Template Matching for Object Detection, Segmentation and Pose Estimation Using Depth Images.
Proceedings of the International Conference on Robotics and Automation, 2019
Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019
Mutual Hypothesis Verification for 6D Pose Estimation of Natural Objects.
Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017
A dual-layer user model based cognitive system for user-adaptive service robots.
Proceedings of the 20th IEEE International Symposium on Robot and Human Interactive Communication, 2011