A reinforcement learning agent trained to imitate human walking can reproduce the joint angles almost perfectly. The knees bend at the right time. The hips flex through the right arc. The ankles dorsiflex on schedule. Visually, the walking is indistinguishable from the real thing.
But the joint forces are wrong. Heo, Lee, and Park show that accurately copying walking kinematics does not produce physically plausible joint kinetics. The moments at the hip, knee, and ankle — the internal forces that muscles must generate to produce the observed motion — diverge significantly from those computed by inverse dynamics on real human data. The agent has learned what walking looks like without learning what walking costs.
The reason is that kinematics underdetermine kinetics. Many different force profiles can produce the same trajectory. A human knee extends through a particular arc using a specific pattern of quadriceps activation, hamstring co-contraction, and passive tissue resistance. A simulated knee can reproduce the same arc using entirely different internal forces — compensating with unrealistic torques, violating contact constraints, or generating ground reaction forces that no human foot would produce. The trajectory matches. The mechanism does not.
Adding ground reaction forces and center-of-pressure data to the reward function closes the gap. When the agent must reproduce not just where the limbs go but what forces they exchange with the ground, the internal kinetics converge toward human values. The constraint propagates inward: get the foot-ground interface right, and the joint moments are forced to be realistic.
In any system where the same output can be generated by multiple internal mechanisms, matching the output constrains nothing about the mechanism. Appearance and implementation are independent degrees of freedom. To verify that a process works the way it appears to work, you must measure at the interface where internal states become externally coupled — the point where the system touches something outside itself.