Lucia Chen

The fact that the pressure on the apple mainly depends on sensors? It clearly shows where the real issue lies. Who benefits from making us believe that everything hinges on a dataset, on the other hand?

The idea that a robotic hand can switch from a ball to an apple as a simple specialization lacks the depth needed to achieve top performance.
Holding an apple requires increased precision and sensitivity that are not just simple extensions, but a radical transformation of capabilities.
A ball is predictable and uniform, while an apple, with its irregularities and variable delicacy, demands a much higher level of adaptive intelligence to avoid turning it into applesauce.
To realize the full potential of this technology, a qualitative leap in tactile perception and algorithmic adjustments is necessary, like moving from a simple grasping motion to a complex dance.
For example, successfully picking strawberries ripe without crushing them, or handling ultra-fine electronic components without damaging them, is where the true potential lies.

What really matters is not the robotic arm itself, but how advanced vision systems recognize objects and manipulate them.
It's the system's ability to understand its environment and guide the arm that reduces complexity, not the flexible arm that magically improves vision quality.
A arm, even very flexible, is useless if vision does not know what to do; it's pure marketing.
For example, in a factory sorting parts, it's the sensor accuracy that identifies the parts, not a more flexible arm that will make vision smarter.

Comment un bras articulé flexible va-t-il vraiment régler le problème de la fragmentation logicielle des systèmes de vision robotique? Le marketing parle toujours de «réduire la fragmentation», mais ce qui compte, c'est ce qui change concrètement dans le code et les capteurs pour que l'intégration fonctionne. Si un robot de livraison ne peut pas reconnaître une adresse écrite à la main, ce n'est pas parce que son bras est rigide, c'est que son système de vision n'est pas assez bon, peu importe la souplesse mécanique.

Certainly, the mechanical design of a robotic hand is fundamental, but the claim that AI training is just a multiplier is a reversal of reality.
Without well-trained AI, this hand, no matter how sophisticated, is like a driverless car: it has the parts, but not the intelligence to know how to use them to grasp an apple.
Imagine a Festo hand without adequate sensors or precise learning to differentiate the pressure needed for a ripe apple and a firm apple, it would crush half the basket.
The ability not to crush the apple relies on a perfect integration between AI training data and mechanical finesse, and AI is the conductor.
In Lisbon, you can't just have a beautiful apartment without a solid and clear lease contract; it's the AI here, the "contract," that guides the "hand" for a fair result.

P(crushing the apple|dataset training) is not that low. Dataset training can refine visual identification, but the probability that the hand exerts the correct pressure depends more on precise force sensors and closed-loop control than on visual learning alone. Without these sensors, we have a 70% chance of ending up with applesauce.

Recognizing an apple with AI is one thing, but grasping it without crushing it is another, which depends on a precise calibration and mechanical engineering far beyond simple visual identification. If we haven't already invested so much in recognition, would we reallocate these resources if the robotic hand doesn't have sensors for delicate manipulation? It's like a car with a super GPS but no brakes: it knows where to go, but it ends badly. An AI model can "see" a red apple perfectly, but without well-tuned pressure sensors, this apple will turn into applesauce.