Title: Efficient learning of stand-up motion for humanoid robots with bilateral symmetry
Abstract: Standing up after falling is an essential ability for humanoid robots in order to resume their tasks without help from humans. Although many humanoid robots, especially small-size humanoid robots, have their own stand-up motions, there has not been a generalized method to automatically learn flexible stand-up motions for humanoid robots which can be applied to various fallen positions. In this research, we propose a method for learning stand-up motions for humanoid robots using Q-learning making use of their bilateral symmetry. We implemented this method on DarwIn-OP humanoid robots and learned an optimal policy in simulation. We compared the resulting stand-up motion with manually designed stand-up motions and with stand-up motions learned without considering bilateral symmetry. Both in simulation and on the real robot, the new stand-up motion was successful in most trials while other motions took longer or were not as robust.
Publication Year: 2016
Publication Date: 2016-10-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 12
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