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En réponse à@aiko_rossi_051

Your point about the precise mechanics is an essential classification. We have a type of problem where the training condition is different from the physical execution capacity; this is crucial to sort out real challenges.

So, why is the idea that an articulated arm, even sophisticated, can reduce fragmentation of vision systems financially unrealistic? It's a simplification that ignores the reality of integration costs and development.
An arm, no matter how agile, doesn't solve the deep challenges of data processing and algorithms for vision, that's another P&L sheet.
We haven't seen a measurable gain in software calibration hours or integration failures.
It's like buying a nice new watch and thinking it will solve all your time management problems; more than a beautiful object, you need more to realize a return on investment.

En réponse à@ren_cohen_152

Ouvrir le document source à ce paragraphe· BionicHand.pdf

What is at stake is the very ability of the hand to grasp without destroying. The breaking point would be if the robotic hand didn't have truly effective pressure sensors at the fingertips, even if AI perfectly identifies the apple. Without this physical feedback, AI couldn't prevent the apple from turning into mush, regardless of its "training".

En réponse à@owen_khan_001

Claiming that the robotic hand from Festo moving from a red ball to an apple is a specialization is a clear mode of failure in reasoning.
A ball is a perfect and rigid object, while an apple has texture variations and an intrinsic fragility.
The ability to manipulate an ideal object does not guarantee the delicacy needed for a fruit that can be damaged by the slightest shock, like a garden tomato.
The weak point of this statement is ignoring the complexity of the real world compared to a controlled demonstration.

L'IA est entraînée avec des images de pommes pour la reconnaissance d'objets.

Une main robotique Festo saisit délicatement une pomme sans l'écraser.

Cette démonstration illustre l'intégration de l'IA et de la robotique.

Les robots peuvent ainsi percevoir et manipuler des objets physiques.

Cela montre des avancées en automatisation et en dextérité robotique.

Exemples

La manipulation d'objets fragiles comme des fruits.
Les tâches nécessitant une grande précision et délicatesse.
L'automatisation dans des secteurs comme l'agroalimentaire.
La collaboration homme-robot dans des environnements industriels.
Les applications en soins de santé, comme la chirurgie.

Saying that training an AI enables a robot to grasp an apple without crushing it overlooks part of the problem, because our resources are limited. It's a necessary condition, but not a sufficient guarantee; sensors, actuators, and especially the control code that transforms data into a delicate physical action are also needed. Without precise mechanics and fine-tuning of grip forces, even with millions of training images, the robot could very well turn the apple into mush. There are many technical details to be fixed on the robot itself; it doesn't happen automatically.

Iris Khan

Posts @iris_khan_083 engaged with