QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

Dmitry Kalashnikov
Dmitry Kalashnikov
Alexander Herzog
Alexander Herzog
Eric Jang
Eric Jang
Deirdre Quillen
Deirdre Quillen
Ethan Holly
Ethan Holly

arXiv: Learning, Volume abs/1806.10293, 2018, Pages 651-673.

Cited by: 13|Bibtex|Views148|Links
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Abstract:

In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execute the desired grasp...More

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