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Edition: Kids. Robots and AI: explore, ask questions and check the facts.
Physical AI — the path to a beautiful tomorrow.
THE REPLICANT TRUTHA NEWSPAPER ABOUT PHYSICAL ARTIFICIAL INTELLIGENCE
Forecast · 2026-11-02 · CET
MORNING · 09:00
9.8°
Cloudy
Wind: N 2.9 m/s · 54%
MIDDAY · 12:00
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Clear
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EVENING · 18:00
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Daily: 8.6–12.5 °C
Precipitation probability: 94%
All we can do is learn to be human while the rain falls.
Issue № 5 ·
rev. 1 · All issues
Robotics research, from training and touch to evaluating skills and practical limitations.
A robot repeating a familiar motion does not necessarily understand a new request. Xiangyu Xu and collaborators introduced a benchmark where a robot arm receives a verbal description of a drawing and chooses a sequence of strokes. Starting from the same blank canvas, multiple drawings and stroke orders can be valid. This reveals more than reproducing one memorized trajectory.
A conventional room map can show a robot where a door is without revealing which way it moves or how much force opening it requires. Dibyendu Ghosh proposes a map that stores probabilistic beliefs about articulated components. After a measured pull, the system updates its estimate, and a planner can ask whether an action is likely to succeed within a stated risk limit.
Human fingers and robotic fingers have different geometry, so copying a hand trajectory does not guarantee the same contact with an object. Haozhe Zhang and collaborators propose a combined hardware-and-software system with a wearable human demonstration interface and a corresponding robotic interface. Both capture visual and fingertip tactile information to establish correspondence across different hand designs.
An instruction such as clearing a table hides many steps: approach, grasp, move and release. Yet large robot datasets often store only the overall task instruction. Xueyuan Wei and collaborators propose a dataset that assigns consistent time-aligned labels to short, or atomic, actions so learning systems can recover the structure of longer behavior.
Robotic hands can have very different numbers of joints yet face similar problems: hold an object, turn it and coordinate both wrists and fingers. Xiaoyu Yang and Nan Wu propose a pipeline combining human-motion pretraining, simulation-based expert learning, robot demonstration collection and visual feedback during execution.
New research emphasizes structured data, measurements and testable results.
Concepts & terms
The key ideas in this issue, explained briefly.