there's been physics simulation of bipedal models learning to walk via reinforcement learning. the results always were a bit choppy and robotic, until they implemented signals propagation delays. this led to more natural, fluid movements that definitely looked more "human". sorry, can't find the video.

personally i absolutely do think that for generating convincingly human-like intelligence you also need some human constraints, otherwise you will get some uncanny valley.

another example would be alpha-zeros play style. AIs don't play like humans, they maximize their chance of winning in the long term without going for good looking opportunities that hurt their chances in the long run (like human players do).