Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is nontrivial to manually design a robot controller that combines these modalities, which have very different characteristics. While deep reinforcement learning has shown success in learnin...

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Bibliographic Details
Published in:	IEEE transactions on robotics Vol. 36; no. 3; pp. 582 - 596
Main Authors:	Lee, Michelle A., Zhu, Yuke, Zachares, Peter, Tan, Matthew, Srinivasan, Krishnan, Savarese, Silvio, Fei-Fei, Li, Garg, Animesh, Bohg, Jeannette
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Clearances Computer simulation Control systems design Deep learning in robotics and automation Haptic interfaces Machine learning perception for grasping and manipulation Reinforcement learning Representations Robot sensing systems Robots sensor fusion sensor-based control Solid modeling Task analysis Visualization
ISSN:	1552-3098, 1941-0468
Online Access:	Get full text
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