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Physical AI — the path to a beautiful tomorrow.
THE REPLICANT TRUTHA NEWSPAPER ABOUT PHYSICAL ARTIFICIAL INTELLIGENCE
Forecast · 2026-11-03 · CET
MORNING · 09:00
9°
Cloudy
Wind: NE 4.4 m/s · 11%
MIDDAY · 12:00
10.4°
Clear
Wind: NE 4.3 m/s · 6%
EVENING · 18:00
8.1°
Clear
Wind: NE 4.1 m/s · 0%
Daily: 5.2–11.1 °C
Precipitation probability: 21%
All we can do is learn to be human while the rain falls.
Issue № 6 ·
rev. 1 · All issues
New studies show how observations and real tests can help robots learn tasks, while the results remain limited to experimental settings.
Imagine watching someone put objects on shelves without being told which movements to make. Learning from that video alone would be difficult for a robot too. A research team led by Jun Cen investigated whether videos could help robots perform tasks that were missing from their usual training examples.
Putting a small network connector into its socket is harder than it looks. The plug must face the right way, line up and enter without slipping. A team led by Ammar Abbas compared ways to teach a five-fingered robot hand this task. They carried out roughly 1,700 attempts on real equipment, not just on a screen.
When you pick up a glass, bending your fingers is only part of the job. They must touch the glass in useful places or it may slip. A team led by Zhixuan Xu studied how to teach robot hands using the changing places where fingers contact an object. Their system includes pictures from cameras near the fingertips and information about contact surfaces.
Imagine a robot following one person down a busy corridor. Someone wearing similar clothes crosses in front of it, and the robot may confuse the two people. A team led by Mingyang Lyu studied how improving a robot's ability to keep following someone affects the safety of its movements.
A small wheeled robot and a larger robot with a different steering system cannot move through a narrow corridor in exactly the same way. The command to turn right produces different motions. A team led by Zhidong Wang proposed teaching one program to plan paths for several kinds of robots while still respecting those differences.
Video and contact information can help learning, but real actions are needed to check the result.
Concepts & terms
The key ideas in this issue, explained briefly.