J
Jay Ming Wong
Researcher at Charles Stark Draper Laboratory
Publications - 11
Citations - 229
Jay Ming Wong is an academic researcher from Charles Stark Draper Laboratory. The author has contributed to research in topics: Markov decision process & Mobile manipulator. The author has an hindex of 5, co-authored 11 publications receiving 186 citations. Previous affiliations of Jay Ming Wong include University of Massachusetts Amherst.
Papers
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Proceedings ArticleDOI
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
Jay Ming Wong,Vincent Kee,Tiffany Le,Syler Wagner,Gian Luca Mariottini,Abraham Schneider,Lei Hamilton,Rahul Chipalkatty,Mitchell Hebert,David M. S. Johnson,Jimmy Wu,Bolei Zhou,Antonio Torralba +12 more
TL;DR: SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects.
Proceedings ArticleDOI
SegICP: Integrated deep semantic segmentation and pose estimation
Jay Ming Wong,Vincent Kee,Tiffany Le,Syler Wagner,Gian Luca Mariottini,Abraham Schneider,Lei Hamilton,Rahul Chipalkatty,Mitchell Hebert,David M. S. Johnson,Jimmy Wu,Bolei Zhou,Antonio Torralba +12 more
TL;DR: In this article, SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects.
Posted Content
Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics.
TL;DR: This manuscript surveys recent work in the literature that pertain to applying deep learning systems to the robotics domain, either as means of estimation or as a tool to resolve motor commands directly from raw percepts and suggests that deep learning as a tools alone is insufficient in building a unified framework to acquire general intelligence.
Proceedings ArticleDOI
Affordance-based Active Belief: Recognition using visual and manual actions
Dirk Ruiken,Jay Ming Wong,Tiffany Q. Liu,Mitchell Hebert,Takeshi Takahashi,Michael W. Lanighan,Roderic A. Grupen +6 more
TL;DR: The impact of the belief-space and object model representations on recognition efficiency and performance is focused on.
Proceedings ArticleDOI
Intrinsically motivated multimodal structure learning
Jay Ming Wong,Roderic A. Grupen +1 more
TL;DR: In this paper, a long-term intrinsically motivated structure learning method for modeling transition dynamics during controlled interactions between a robot and semipermanent structures in the world has been presented, which serve as the basis for a number of possible future tasks defined as Markov Decision Processes (MDPs).