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Edition: Engineer. Technology, experiments and limitations.
Physical AI — the path to a beautiful tomorrow.
THE REPLICANT TRUTHA NEWSPAPER ABOUT PHYSICAL ARTIFICIAL INTELLIGENCE
Forecast · 2026-11-01 · CET
MORNING · 09:00
9.2°
Cloudy
Wind: NW 0.6 m/s · 0%
MIDDAY · 12:00
10.7°
Cloudy
Wind: N 1.4 m/s · 0%
EVENING · 18:00
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Daily: 7.3–11.3 °C
Precipitation probability: 8%
All we can do is learn to be human while the rain falls.
Issue № 4 ·
rev. 3 · Archive
New robot control methods, evaluation protocols and their reproducibility limits.
A high success rate in a demonstration does not tell a factory buyer whether a robot model is affordable to deploy. Adaptation costs, computing requirements and the credibility of the experiments also matter. Xin Ma and colleagues introduced RoboSeed, a framework for assessing models that turn images and instructions into actions alongside models that predict physical developments.
Adoption decisions must separate research results from guaranteed operational performance. Separates sequential execution reliability from individual manipulation skills and can sit above existing VLA controllers. Evidence is confined to selected dual-arm tasks; real-robot success was 17 of 30 trials, not full reliability.
Adoption decisions must separate research results from guaranteed operational performance. Existing collision checkers can potentially constrain generative action policies even when they are not differentiable. One simulation task; per-action-chunk inference time rose from 0.071 to 1.345 seconds.
A robot may recognize an object correctly yet place its gripper slightly off target. Rigid fingers can lose contact, while a soft gripper may deform around the object. Researchers led by Kurt Enkera introduced SimTO-Robust, a computer-aided design method intended to make grippers tolerant of such errors through their physical shape.
A humanoid that walks to a table must also stop, bend, reach and recover its balance without switching to an entirely new motor system. Yet many robotics pipelines train separate controllers for each behavior. Chen Tessler and colleagues introduced Metatron, a programmable whole-body execution layer that pairs one trained generative motor policy with software-defined skills. The research was submitted on October 27, 2026; a later listing date should not be mistaken for its original publication date.
Research and demonstrations expand the range of robot actions, but the strength of the result depends on test conditions.
Concepts & terms
The key ideas in this issue, explained briefly.