Can AI learn to grow its body and its movement at the same time?
In short: Human children don't just learn to walk; they grow into the bodies capable of walking. Replicating this dual process in artificial systems is a massive hurdle for robotics, where machines must master both physical structure and complex motor skills to achieve true biological versatility.
In biological systems, locomotion is a product of two intertwined processes: morphology and motor skill development. Morphology refers to the physical form and structure of the body, while motor skills are the specific tasks an individual performs. For humans, these are not learned in isolation. Infants begin with rudimentary movements and gradually advance to sophisticated actions like running as their physical bodies mature. This transition from childhood to early adulthood allows for a continuous refinement of movement proficiency and versatility.
Replicating this in artificial bipedal systems presents a significant challenge for reinforcement learning (RL). While many robots use torque-actuated joints, musculoskeletal humanoid systems are far more complex. These systems use muscles that can only contract, requiring a coordinated agonist-antagonist setup to prevent forces from canceling each other out. This creates an over-actuated system where there are more muscles than degrees of freedom, making the control problem incredibly intricate. Furthermore, the chemical properties of muscles act as a low-pass filter, meaning short twitches often fail to produce the necessary joint motions.
To bridge this gap, researchers like Badie and colleagues have proposed a curriculum that mimics human development. Instead of forcing a static robot to learn complex tasks immediately, this approach combines physical growth with a structured progression of skills. By starting with simple behaviors—such as standing or stepping—and systematically increasing task complexity, the system can navigate the vast state space more efficiently. This method aims to avoid the common pitfalls of multi-task learning, such as slow convergence or instability, by providing the structured progression necessary for mastering diverse terrains and varying speeds.
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Sources
- Summary by Intellect · source: AI Learning Morphology and Movement...at the Same Time!
- Also: Bioinspired morphology and task curricula for learning locomotion in bipedal muscle-actuated systems - Communications Engineering
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