Vision gives a robot arm a map but it doesn't give it a grip.

Try to pick up an egg with only camera input and zero force feedback, you either crush it or drop it. There's no signal telling the actuator "you're at 80% of fracture pressure, ease off."
That signal only exists as haptic data: force, torque, tactile pressure, slip detection.
This is why the "Embodied AI bottleneck" isn't a vision problem anymore. It's a touch problem. You can have a perfect 3D model of a doorknob and still fail to turn it if the gripper doesn't know how hard it's gripping or when it's slipping.
That's the problem @InvLambda's Teleop-to-Earn model is built to solve. Instead of hoping simulation can approximate real-world friction and compliance, T2E puts human operators in the loop; real hands, real dexterity, generating the multimodal (visual + haptic) datasets that sim environments can't fake.
Vision tells a robot where the world is.
Haptics tell it how to touch it.
The second one is the harder problem and the one actually getting solved right now.
@InvLambda #InvertedLambdaTheBreach #InvertedLambda #Robotics #Teleoperation #SecondContact #SecondContactTheBreach