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20152022
most citedHow to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned

565 citations · 4.4k across the 119 of their papers we have counts for

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Showing 2017Show all

16 papers · 1 filter

cs.CV201764 cited

Unifying Map and Landmark Based Representations for Visual Navigation

Saurabh Gupta, David Fouhey, Sergey Levine +1

This works presents a formulation for visual navigation that unifies map based spatial reasoning and path planning, with landmark based robust plan execution in noisy environments.…

cs.CV201744 cited

Sim2Real View Invariant Visual Servoing by Recurrent Control

Fereshteh Sadeghi, Alexander Toshev, Eric Jang +1

Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints and angles, even in the presence of optical distortions. In robotics, this abi…

cs.LG201724 cited

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

Benjamin Eysenbach, Shixiang Gu, Julian Ibarz +1

Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a large amount of experience to be collected by the…

cs.CL2017

Learning with Latent Language

Jacob Andreas, Dan Klein, Sergey Levine

The named concepts and compositional operators present in natural language provide a rich source of information about the kinds of abstractions humans use to navigate the world. Ca…

cs.RO201724 cited

Learning Robotic Manipulation of Granular Media

Connor Schenck, Jonathan Tompson, Dieter Fox +1

In this paper, we examine the problem of robotic manipulation of granular media. We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions.…

cs.RO2017112 cited

Self-Supervised Visual Planning with Temporal Skip Connections

Frederik Ebert, Chelsea Finn, Alex X. Lee +1

In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning…