Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

Robotics: Science and Systems, 2018.

Cited by: 62|Bibtex|Views166|DOI:https://doi.org/10.15607/rss.2018.xiv.010
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Designing agile locomotion for quadruped robots often requires extensive expertise and tedious manual tuning. In this paper, we present a system to automate this process by leveraging deep reinforcement learning techniques. Our system can learn quadruped locomotion from scratch using simple reward signals. In addition, users can provide a...More

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