Embodiment-oriented thinking

What happens next?

Most video models learn the scene. Robotics needs a model of consequence. Change the conditions below and inspect a transparent next-state projection.

Scene conditions

Educational inputs, not a live model feed.

Projected next state

A simple, inspectable rule-based projection.

Robot scene

+
Approach and prepare gripLow obstacle risk supports forward motion.
76%
28 cm
This lab teaches the idea of predicting consequences. It does not run LingBot-Video, access a robot, or claim live inference.

A local rule projects position and action labels

Read the explanation

The source starts at distance forty centimeters and motion intent seventy. Its displayed position is the rounded maximum of zero and distance minus intent divided by ten: thirty three centimeters. Bars share ten pixels per centimeter, four hundred pixels before and three hundred thirty after. The static saved HTML says twenty eight, but the initialized formula yields thirty three. This is an educational rule over entries, not live model inference, robot movement or a physical motion law. Default risk twenty contributes one hundred minus twenty, or eighty. Grip eighty and intent seventy join it: two hundred thirty divided by three, rounded to seventy seven percent. Bars share four pixels per term unit, three hundred twenty, three hundred twenty and two hundred eighty pixels. This displayed confidence is a source arithmetic label, not a calibrated success probability or safety assessment. The saved static seventy six is replaced by the initialized formula. With grip thirty nine, risk sixty selects reposition for a safer grip. Risk sixty one instead selects pause and scan, because risk greater than sixty is checked first. Bars share six pixels per risk unit: three hundred sixty and three hundred sixty six. Grip below forty is considered only when risk is at most sixty. At grip forty with low risk, the default approach label returns. Labels express this rule only, not validated robot safety, collision avoidance or a prediction learned from video.

Super generates helpful tools and automates fact-checking across the internet proactively. If you enjoyed this tool, build your own with Super and share it with a friend.