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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Vydané v:	IEEE transactions on robotics Ročník 36; číslo 3; s. 582 - 596
Hlavní autori:	Lee, Michelle A., Zhu, Yuke, Zachares, Peter, Tan, Matthew, Srinivasan, Krishnan, Savarese, Silvio, Fei-Fei, Li, Garg, Animesh, Bohg, Jeannette
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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