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    Tag: PPO

    Sep 14
    14 min read
    Imitation Learning vs. Reinforcement Learning: Which Yields Better Dexterous Manipulation?

    Building an anthropomorphic humanoid hand with 16 to 24 degrees of freedom is an extraordinary mechatronic achievement. Controlling it to perform high-dexterity industrial tasks—such as seating a rubber O-ring into a groove, threading an M6 bolt into a blind hole, or manipulating a flexible wiring harness—remains the hardest software challenge in robotics. Unlike bipedal locomotion, […]

    Sep 14
    17 min read
    Deep Reinforcement Learning for Locomotion: How Humanoids Learn to Walk on Uneven Terrain

    For decades, the standard approach to bipedal locomotion was anchored in classical analytical mechanics: Zero Moment Point (ZMP) stability, Linear Inverted Pendulum Models (LIPM), and dynamic Model Predictive Control (MPC). Pioneered by platforms like Honda ASIMO and Boston Dynamics Atlas, these analytical controllers solved systems of differential equations to guarantee that the robot’s ground reaction […]