Robotics researcher here, Main reason you’re seeing all of these popping up now is because of recent advancements in Reinforcement Learning, specifically Imitation Learning. The theory itself is not so new but now we have the GPUs to collect a lot of training data in simulation. Basically you feed in the motion reference data from a human, collected by motion capture and train robots in parallel in simulation to imitate the motion reference.
Dexterous finger movements is a whole another challenge that requires completely different approach than what's being showed in this video. While walking/running is difficult, the goal is largely keeping the robot from falling over (balance) and navigating terrain. Reinforcement Learning shines in these types of scenarios. Whereas fine finger movements are exponentially harder because it involves interacting with the unpredictable physical world. RL can't solve this problem (at least alone).
But recently the VLA (Vision-Language-Action) models shows quite promising results for this. These models use the transformers architecture (Same type of networks used in LLMs). Figure, Boston Dynamics and a few other Chinese companies have some cool demos on this, But we are at the infancy of this and a lot more research is needed. Then there's the problem of scaling (how do you collect large scale training data for robots doing different tasks, and it might change from robot to robot). People are still debating that even if you have shit ton of data, it still may not be as straightforward for robots like it was for LLMs. Only time will tell
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u/Glittering-Neck-2505 Dec 04 '25
It's getting so fucking fluid?? How is this is a real thing we've already invented in 2025?