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Legged robots have shown promise in locomotion complex environments, but recovery from falls on challenging terrains remains a significant hurdle.
“Discovery of complex behaviors through contact-invariant optimization”
Igor Mordatch, Emanuel Todorov and Zoran Popović · 2012
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“Partially observable Markov decision processes”
Matthijs Spaan · 2012
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
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“Balance recovery of a quadruped robot”
Mahdi Khorram and S Moosavian · 2015
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“Proximal policy optimization algorithms”
John Schulman et al · 2017
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“Slip detection and recovery for quadruped robots”
Michele Focchi et al · 2018
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“Falling prediction and recovery control for a humanoid robot”
Tianqi Yang et al · 2018
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“Learning agile and dynamic motor skills for legged robots”
Jemin Hwangbo et al · 2019
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“Robust recovery controller for a quadrupedal robot using deep reinforcement learning”
Joonho Lee, Jemin Hwangbo and Marco Hutter · 2019
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“Learning quadrupedal locomotion over challenging terrain”
Joonho Lee et al · 2020
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“Time-varying model predictive control for highly dynamic motions of quadrupedal robots”
Gabriel García, Robert Griffin and Jerry Pratt · 2021
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“Isaac gym: High performance gpu-based physics simulation for robot learning”
Viktor Makoviychuk et al · 2021
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“Legged robots that keep on learning: Fine-tuning locomotion policies in the real world”
Laura Smith et al · 2022
Later among the works it cites.
“Dribblebot: Dynamic legged manipulation in the wild”
Yandong Ji, Gabriel Margolis and Pulkit Agrawal · 2023
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“Robust recovery motion control for quadrupedal robots via learned terrain imagination”
I Nahrendra et al · 2023
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Ziwen Zhuang et al · 2023
Later among the works it cites.
“Dynamic Fall Recovery Control for Legged Robots via Reinforcement Learning”
Sicen Li et al · 2024
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Xin Wang, Yudong Chen and Wenwu Zhu · 2021
Cited alongside, same era.
Zeren Luo et al · 2024
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