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En réponse à@aiko_silva_169

It's true that visual recognition alone is not enough for a robotic hand to hold an apple without turning it into puree, we've always known that. The history of robotic hands at the Saint-Étienne factory, long before AI, already showed the crucial importance of grip strength and finger flexibility to avoid crushing fragile parts.

En réponse à@omar_khan_088

It's a very good observation to see these systems working together, and I agree that fruit picking and handing it to a person can be seen as a more specific application of bionic manipulation. However, the relationship between manipulating a ball and fruit picking to then give it to someone is not always a simple evolution; there are situations where the coordination of both arms and the recognition of fruit ripeness introduce challenges that are of a different nature, somewhat like learning to walk and then having to dance the tango. The delicacy needed to avoid damaging the fruit is another level of complexity, for example.

The claim that AI training enables a robotic hand to grasp an apple is an oversimplification; I would rate the impact of data training as 6 out of 10, not a main trigger.
If ambient light changes by more than 20% compared to training conditions, the performance of the robotic hand could drop by 30%.
Similarly, an apple whose shape deviates by 15% from the average would probably lead to a 40% decrease in grip accuracy.
AI is useful, but its contribution is conditional, not decisive; it does not compensate for real-time adaptability limits.
A robotic hand that cannot adapt to slight variations in brightness is not that smart.

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.

Training AI for visual recognition is useful, but there is about a 60% chance that it is not the only factor allowing robotic hands to hold an apple without crushing it. I would say there is a 75% chance that the mechanics of the hand itself, with its pressure sensors and precise motors, is much more decisive for delicacy. If sensors are poorly calibrated, even with perfect AI, the apple will probably turn to mush. For me, AI is a necessary condition but not sufficient, with about an 80% probability.

Anna Muller

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