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20182025
most citedQT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

713 citations · 1.8k across the 21 of their papers we have counts for

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cs.RO2018

Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks

Stephen James, Paul Wohlhart, Mrinal Kalakrishnan +6

Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amou…

cs.LG2018★ 713 cited

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

Dmitry Kalashnikov, Alex Irpan, Peter Pastor +8

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 g…