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Robust Reference-Free Sim-to-Real Reinforcement Learning for Bipedal Locomotion

Creator:

Siekmann, Jonah A.

Abstract:

In recent years, model-free Deep Reinforcement Learning (RL) has become an increasingly popular alternative to more traditional model-based or optimization-based control methods in solving robotic legged locomotion. However, deploying RL in the real world can be a significant undertaking. Constructing reward functions which compel controllers to learn the desired behavior...

Resource Type:

Masters Thesis
Agile Bipedal Locomotion via Hierarchical Control by Incorporating Physical Principles, Learning, and Optimization

Creator:

Green, Kevin

Abstract:

Robotic Bipedal locomotion holds the potential for efficient, robust traversal of difficult terrain. The difficulty lies in the dynamics of locomotion which complicate control and motion planning. Bipedal locomotion dynamics are dimensionally large problems, extremely nonlinear, and operate on the limits of actuator capabilities, which limit the performance of generic...

Resource Type:

Dissertation

Full Text:

Michigan Medical School, Glenn Green, David Zopf and Kyle VanKoevering. Thank you for giving me the oppor