(Nazarbayev University School of Science and Technology, 2017-04) Keldibek, Amina
Humanoid robots are developed around the world with the purpose to assist humans in their
domestic and public activities and operate in unstructured and hazardous environments. To
accomplish this effectively, intelligent humanoids should be autonomous, to accomplish
high-level human tasks without help, and adaptable, to be able to react to dynamic changes
and external disturbances in operating environments. The primary objective of this thesis is
to investigate how biologically plausible methods such as reservoir computing and rewardmodulated
learning can be used for generating robust sensory-motor outputs and achieving
adaptability of the biped system. Recurrent neural networks architecture is studied on
two robot systems: first is Asimo humanoid and second is biped developed at Nazarbayev
University.