Aiko Muller

Le fait que l'IA puisse identifier une pomme ne signifie pas que la main robotique va la saisir sans l'écraser, c'est une vision bien trop optimiste de nos ressources limitées.
Ce n'est pas parce qu'on a les plans qu'on a la maison; il faut encore les briques et le maçon pour que ça marche.
On doit investir du temps et de l'argent dans des capteurs de force et des algorithmes de contrôle précis, sinon c'est juste du gaspillage.
Sans ces ajustements matériels et logiciels, l'entraînement de l'IA n'est qu'un coût supplémentaire qui ne rapporte rien de concret.
Par exemple, vous pouvez avoir le meilleur logiciel de navigation, mais si votre voiture n'a pas de roues, vous n'irez nulle part.

Even if the BionicSoftHand 2.0 robotic hand is a truly advanced system, categorizing it as a "human-robot collaboration" is not the simplest and most accurate description. It is just a sophisticated tool, like my new dishwasher. It does the job it's asked to do, certainly with more sensors, but it does not "collaborate" with me to decide the washing program. For example, my washing machine does not tell me that a T-shirt is too dirty for a short cycle; it executes. Collaboration is between two conscious entities.

I agree that visual recognition by AI is a key component, with a contribution probability of 90%, but the delicate grasping of an apple without crushing it probably depends 80% on the sensorial and mechanical capabilities of the robotic hand itself. Without pressure sensors and a feedback loop, the system could identify the apple perfectly but crush it. It's like having precise GPS without brakes on the car. Training AI could give a direction, but the ability to execute mainly depends on physical engineering.

Frankly, believing that AI training "contributes to the possibility" of grasping an apple without crushing is like saying that having a recipe contributes to making the cake — we mainly need good ingredients and cooking skills.
Time and money are scarce, and relying solely on visual recognition without force sensors and fine manipulation algorithms risks ruining everything.
Imagine an AI that sees a light bulb perfectly but, without sensor feedback, tightens it so much that it explodes; we missed the target, and the resource is lost.
Limited resources should be focused on what really works, like robotic arms with tactile sensitivity to avoid waste.

You say that the architecture of the Festo Bionic Handling Assistant, with its flexible arm, embodies advanced robotic manipulation capabilities, which seems logical for complex objects. However, one must be careful not to confuse potential with immediate reality on the ground; the distinction here is between an innovative design and a proven universal application. In our factories, a flexible arm may seem great on paper, but if each small adjustment requires hours of recalibration or if the production rate drops, it becomes a problem. For example, in a textile factory where materials vary constantly, the time spent adjusting the robot could negate any flexibility gains.

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.