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20152023
most citedHow to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned

565 citations · 7k across the 202 of their papers we have counts for

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Showing 2019 · cs.ROShow all

15 papers · 2 filters

cs.RO2019

Morphology-Agnostic Visual Robotic Control

Brian Yang, Dinesh Jayaraman, Glen Berseth +2

Existing approaches for visuomotor robotic control typically require characterizing the robot in advance by calibrating the camera or performing system identification. We propose M…

cs.RO2019

AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos

Laura Smith, Nikita Dhawan, Marvin Zhang +2

Robotic reinforcement learning (RL) holds the promise of enabling robots to learn complex behaviors through experience. However, realizing this promise for long-horizon tasks in th…

cs.RO2019★ 15 cited

Contextual Imagined Goals for Self-Supervised Robotic Learning

Ashvin Nair, Shikhar Bahl, Alexander Khazatsky +3

While reinforcement learning provides an appealing formalism for learning individual skills, a general-purpose robotic system must be able to master an extensive repertoire of beha…

cs.RO2019★ 16 cited

RoboNet: Large-Scale Multi-Robot Learning

Sudeep Dasari, Frederik Ebert, Stephen Tian +6

Robot learning has emerged as a promising tool for taming the complexity and diversity of the real world. Methods based on high-capacity models, such as deep networks, hold the pro…

cs.RO2019

Scaled Autonomy: Enabling Human Operators to Control Robot Fleets

Gokul Swamy, Siddharth Reddy, Sergey Levine +1

Autonomous robots often encounter challenging situations where their control policies fail and an expert human operator must briefly intervene, e.g., through teleoperation. In sett…

cs.RO2019★ 68 cited

Deep Dynamics Models for Learning Dexterous Manipulation

Anusha Nagabandi, Kurt Konoglie, Sergey Levine +1

Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also not…