Push Recovery Control for Humanoid Robot Using Reinforcement Learning

2019 Third IEEE International Conference on Robotic Computing (IRC) ◽

10.1109/irc.2019.00102 ◽

2019 ◽

Cited By ~ 2

Author(s):

Harin Kim ◽

Donghyeon Seo ◽

Donghan Kim

Keyword(s):

Reinforcement Learning ◽

Humanoid Robot ◽

Push Recovery

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Push Recovery Strategies through Deep Reinforcement Learning

2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE) ◽

10.1109/lars/sbr/wre51543.2020.9306967 ◽

2020 ◽

Author(s):

Dicksiano Carvalho Melo ◽

Marcos R. O. A. Maximo ◽

Adilson Marques da Cunha

Keyword(s):

Reinforcement Learning ◽

Push Recovery ◽

Recovery Strategies

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Hierarchical deep reinforcement learning to drag heavy objects by adult-sized humanoid robot

Applied Soft Computing ◽

10.1016/j.asoc.2021.107601 ◽

2021 ◽

pp. 107601

Author(s):

Saeed Saeedvand ◽

Hanjaya Mandala ◽

Jacky Baltes

Keyword(s):

Reinforcement Learning ◽

Humanoid Robot

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Humanoid robot push recovery through walking phase modification

2010 IEEE Conference on Robotics, Automation and Mechatronics ◽

10.1109/ramech.2010.5513130 ◽

2010 ◽

Cited By ~ 7

Author(s):

Albertus Hendrawan Adiwahono ◽

Chee-Meng Chew ◽

Weiwei Huang ◽

Van Huan Dau

Keyword(s):

Humanoid Robot ◽

Phase Modification ◽

Push Recovery

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Push Recovery of a Position-Controlled Humanoid Robot Based on Capture Point Feedback Control

2017 5th RSI International Conference on Robotics and Mechatronics (ICRoM) ◽

10.1109/icrom.2017.8466226 ◽

2017 ◽

Author(s):

Milad Shafiee-Ashtiani ◽

Aghil Yousefi-Koma ◽

Reihaneh Mirjalili ◽

Hessam Maleki ◽

Mojtaba Karimi

Keyword(s):

Feedback Control ◽

Humanoid Robot ◽

Push Recovery ◽

Capture Point

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Teaching a humanoid robot to walk faster through Safe Reinforcement Learning

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2019.103360 ◽

2020 ◽

Vol 88 ◽

pp. 103360 ◽

Cited By ~ 4

Author(s):

Javier García ◽

Diogo Shafie

Keyword(s):

Reinforcement Learning ◽

Humanoid Robot

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A Receding Horizon Push Recovery Strategy for Balancing the iCub Humanoid Robot

Advances in Service and Industrial Robotics - Mechanisms and Machine Science ◽

10.1007/978-3-319-61276-8_33 ◽

2017 ◽

pp. 297-305

Author(s):

Stefano Dafarra ◽

Francesco Romano ◽

Francesco Nori

Keyword(s):

Humanoid Robot ◽

Recovery Strategy ◽

Receding Horizon ◽

Push Recovery

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Closed-Loop Push Recovery for an Inexpensive Humanoid Robot

Lecture Notes in Computer Science - Recent Trends and Future Technology in Applied Intelligence ◽

10.1007/978-3-319-92058-0_22 ◽

2018 ◽

pp. 233-244 ◽

Cited By ~ 2

Author(s):

Amirhossein Hosseinmemar ◽

Jacky Baltes ◽

John Anderson ◽

Meng Cheng Lau ◽

Chi Fung Lun ◽

...

Keyword(s):

Humanoid Robot ◽

Closed Loop ◽

Push Recovery

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Off-line path integral reinforcement learning using stochastic robot dynamics approximated by sparse pseudo-input Gaussian processes: Application to humanoid robot motor learning in the real environment

2013 IEEE International Conference on Robotics and Automation ◽

10.1109/icra.2013.6630740 ◽

2013 ◽

Cited By ~ 1

Author(s):

Norikazu Sugimoto ◽

Jun Morimoto

Keyword(s):

Reinforcement Learning ◽

Motor Learning ◽

Path Integral ◽

Gaussian Processes ◽

Humanoid Robot ◽

Robot Dynamics ◽

The Real ◽

Real Environment ◽

Line Path

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Momentum-based reactive stepping controller on level and non-level ground for humanoid robot push recovery

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems ◽

10.1109/iros.2011.6048132 ◽

2011 ◽

Cited By ~ 4

Author(s):

Seung-kook Yun ◽

Ambarish Goswami

Keyword(s):

Humanoid Robot ◽

Push Recovery ◽

Reactive Stepping

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Deep Reinforcement Learning for Humanoid Robot Dribbling**Alexandre Muzio acknowledges CAPES for his scholarship (number 88882.161989/2017-01). Takashi Yoneyama would like to thank CNPq - National Research Council of Brasil for the grant 304134/2-18-0. The authors thank ITAndroids' sponsors: Altium, Cenic, Intel, ITAEx, MathWorks, Metinjo, Micropress, Polimold, Rapid, SolidWorks, STMicroelectronics, Wildlife Studios, and Virtual.PYXIS.

2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE) ◽

10.1109/lars/sbr/wre51543.2020.9307084 ◽

2020 ◽

Author(s):

Alexandre F. V. Muzio ◽

Marcos R.O. A. Maximo ◽

Takashi Yoneyama

Keyword(s):

Reinforcement Learning ◽

Humanoid Robot ◽

Research Council ◽

National Research Council

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