Reinforcement learning (RL) offers a promising solution for controlling humanoid robots, particularly for bipedal locomotion, by learning adaptive and flexible control strategies. However, direct RL application is hindered by time-consuming trial-and-error processes, necessitating training in simulation before real-world transfer. This introduces a reality gap that degrades … [Read more...] about Bridging the Reality Gap: Analyzing Sim-to-Real Transfer Techniques for Reinforcement Learning in Humanoid Bipedal Locomotion
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