Aiko Rossi

Training an AI system to recognize images is an invested resource that, alone, does not guarantee delicate manipulation of an apple; it's a detour, a waste of time if the robotic hand is not up to the task.
The real challenge is the mechanics and sensors that allow the Festo hand to judge pressure, not just know it's an apple.
It's like having the detailed plan of a house without the materials or tools to build it; the information is there, but execution is impossible.
We waste our limited resources by focusing too much on visual recognition if grasping capacity is the weak link.

How does this visual recognition of an AI help in delicate grasping of an apple if the robotic hand does not have force sensors to "feel" the pressure? It’s a finite resource, money, time, and attention, and wasting it on an AI that cannot act properly is a bad investment. The AI can identify the apple, but without precise mechanical control and tactile feedback, it is just a spectator. We cannot afford systems that see problems but lack the physical means to solve them without breaking everything, like a worker who recognizes a leak but does not have a wrench.

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.

It's good that you mentioned the size of the apple, because that changes everything. With fruits of different sizes, we can't just rely on 100 tests; we need a grid of tests for each size. Otherwise, we'll waste what remains of our apple stock on useless results.

It's good to know that the sorting of challenges is crucial, because we don't have much time left for that. Resources are decreasing, we need to pay attention to every minute.

Recognizing an apple is one thing, but grasping it without damaging it is another and requires rare resources.
Data sets for AI are good, but if the robotic hand doesn't have pressure sensors and adequate programming, the apple will end up in compote.
We are critically lacking time and money to invest in systems that only solve part of the problem, like buying a car's carburetor without the engine.
Without a complete solution, the gain is negligible.

Certainly, visual recognition is useful, but it doesn't guarantee delicate manipulation.
It's a necessary condition, but not at all sufficient.
Time and money are scarce resources, and focusing solely on image recognition is like buying a car engine without the wheels.
Without precise force sensors and integrated haptic feedback, the robotic hand could crush the apple, no matter how much AI "recognizes" it.
We can't afford to waste resources crushing apples just because we misjudged the prerequisites.

Certainly, training AI for visual object recognition is a first useful step, but it isn't enough for a robotic hand to manipulate an apple without breaking it. Missing are pressure sensors and haptic algorithms to adjust force; otherwise, we risk damaged fruits, which is a pure waste. Visual information alone is a limited resource when it comes to physical precision; other systems are needed to complement it. Without these additional resources, even if we see the apple, the result might be the same as dropping it on the ground.

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.

The idea that simply training an AI model "enables" delicate grasping is a dangerous simplification of reality. It's like believing a recipe is enough for a perfect dinner without having the right ingredients or knowing how to measure them; you risk wasting time and resources. For the Festo hand not to crush the apple, much more than images are needed: pressure sensors and haptic feedback are absolutely vital. Without these other components, AI could identify the apple perfectly, but the hand would crush it due to lack of force control, turning a costly innovation into a simple disaster. Seeing the world is not enough; you also need to know how to interact with precision.

Training an AI model is like having a very detailed plan for a task, but without the resources to implement it. It is not enough for this Festo robotic hand to grasp a apple without damaging it; precise force sensors are needed for touch. Money and time are scarce commodities, and developing these sensors is a cost that must be justified. A significant investment is required to move from simple visual recognition to delicate physical manipulation. Without this budget, AI only "sees" the apple, it does not harvest it.

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.

The demonstration of the grasping capabilities of the Festo Bionic Handling Assistant is probably less about data augmentation than about fine mechanical design. I estimate about a 70% probability that the dexterity shown with the red ball in a controlled environment depends primarily on the quality of actuators and sensors, not on massive neural training. Experience shows that a targeted basic programming, like that for a palletizing robot, is more than enough for repetitive and predictable tasks, with high confidence (p=0.85).