“Robot learning” papers · AI for Research

Robot learning consists of a multitude of machine learning approaches, particularly reinforcement learning, inverse reinforcement learning and regression methods. These methods have been adapted sufficiently to domain to achieve real-time learning in complex robot systems such as helicopters, flapping-wing flight, legged robots, anthropomorphic arms, and humanoid robots.

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Robot Learning

J. Peters, J. Morimoto, R. Tedrake et al. · MPG.PuRe (Max Planck Society) · 2009

Creating autonomous robots that can learn to act in unpredictable environments has been a long-standing goal of robotics, artificial intelligence, and the cognitive sciences. In contrast, current commercially available industrial and service robots mostly execute fixed tasks and exhibit little adaptability. To bridge this gap, machine learning offers a myriad set of methods, some of which have already been applied with great success to robotics problems. As a result, there is an increasing interest in machine learning and statistics within the robotics community. At the same time, there has been a growth in the learning community in using robots as motivating applications for new algorithms and formalisms. Considerable evidence of this exists in the use of learning in high-profile competitions such as RoboCup and the Defense Advanced Research Projects Agency (DARPA) challenges, and the growing number of research programs funded by governments around the world.

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Robot Learning

Jonathan H. Connell, Sridhar Mahadevan · 1993

Cited by 107

Robot learning

Markevich, I., Vasilyeva, T. · Repository of Belarusian National Technical University (Belarusian National Technical University) · 2019

Cited by 0

Robot Learning

Jan Peters, Russ Tedrake, Nick Roy et al. · Encyclopedia of Machine Learning and Data Mining · 2017

Cited by 3

Robot Learning

Peters, J., Tedrake, R., Roy, N. et al. · MPG.PuRe (Max Planck Society) · 2011

Robot learning consists of a multitude of machine learning approaches, particularly reinforcement learning, inverse reinforcement learning and regression methods. These methods have been adapted sufficiently to domain to achieve real-time learning in complex robot systems such as helicopters, flapping-wing flight, legged robots, anthropomorphic arms, and humanoid robots.

Cited by 0

Robot Learning

WORLD SCIENTIFIC eBooks · 2024

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Robot Learning

M. Moshgbar · Microprocessors and Microsystems · 1994

Cited by 0

Robot Learning

J. Peters, David Lee, J. Kober et al. · MPG.PuRe (Max Planck Society) · 2016

Cited by 0

Robot Learning

Peters, J., Morimoto, J., Tedrake, R. et al. · MPG.PuRe (Max Planck Society) · 2009

Creating autonomous robots that can learn to act in unpredictable environments has been a long-standing goal of robotics, artificial intelligence, and the cognitive sciences. In contrast, current commercially available industrial and service robots mostly execute fixed tasks and exhibit little adaptability. To bridge this gap, machine learning offers a myriad set of methods, some of which have already been applied with great success to robotics problems. As a result, there is an increasing interest in machine learning and statistics within the robotics community. At the same time, there has been a growth in the learning community in using robots as motivating applications for new algorithms and formalisms. Considerable evidence of this exists in the use of learning in high-profile competitions such as RoboCup and the Defense Advanced Research Projects Agency (DARPA) challenges, and the growing number of research programs funded by governments around the world.

Cited by 0

Robot Learning

J. Peters, R. Tedrake, N. Roy et al. · MPG.PuRe (Max Planck Society) · 2011

Cited by 0

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