There’s a scene in the video where a human bends down to grab an object — and the robot mimics the move with uncanny precision. This isn’t a dance. It’s real-time control. Millisecond by millisecond. Smooth, uninterrupted, and incredibly human.
This is #TWIST, a breakthrough from researchers at #StanfordUniversity working on next-generation #HumanoidRobots. The goal? Not to make robots move like us — but to make them move as us.
They’ve created a two-step system: first, a #NeuralNetwork trained to anticipate future movements, predicting what the human body will do next. Then they compress the model so the robot can respond instantly, relying only on live input. The result is what they call biomechanical telepathy. You move — and the robot follows, intuitively.
The tech runs on platforms like the #UnitreeG1 robot and enables complex actions: grabbing objects, squatting, kicking — all controlled via high-fidelity #MotionCapture sensors. It’s a major leap beyond standard RGB vision systems used in other setups like #H20.
There are still limits. The system doesn’t yet offer tactile feedback. The robot tends to overheat with extended use. But the implications are massive: we’re shifting from programming robots to letting them learn through imitation.
And this isn’t sci-fi. This is happening now — at the intersection of #AI, #ReinforcementLearning, robotics, and human-machine interaction.
So the question is no longer how do we teach robots to move?
It’s what happens when they learn directly from us — all the time?
