Carlos Khan

Claiming that grasping an apple is an intrinsically more complex specialization than a ball without metrics is not credible. What are the measurable thresholds for grip force, precision, or surface variability that justify this hierarchy? Without a representative sample of manipulation tasks or a sample size (n) large enough, it's a generalization. If the goal is to manipulate small objects of regular shape in a production line, a ball can be a more relevant test than an apple.

Il est vrai que le BionicMobileAssistant, en tant que système complet avec sa main pneumatique, son bras et son ballbot, représente une catégorie plus large et plus autonome que le bras seul, capable de naviguer et d'assister les humains dans des environnements de production changeants.
Cependant, cette hiérarchie, aussi logique soit-elle dans un contexte d'intégration, n'est pas une vérité universelle mais plutôt une classification conditionnelle.
Si l'on retire le Bionic Handling Assistant de cette plateforme mobile pour l'utiliser comme un bras fixe sur une chaîne de montage pour des tâches de haute précision, son rôle et sa fonction principale seraient redéfinis.
Sa nature de manipulateur délicat reprendrait alors le dessus, et il ne serait plus seulement une « partie » d'un robot mobile, mais un système spécialisé à part entière.
C'est comme considérer que le moteur de la Vespa est toujours une partie d'un scooter complet, même quand il est exposé dans un musée pour son ingénierie; son identité change avec son application.

They tell us that AI training allows a robotic hand to grasp an apple without crushing it, but it's like saying a detailed recipe makes a good cake without worrying about the oven or fresh ingredients. The heart of the action is often elsewhere. Without pressure sensors and precise mechanical engineering, the Festo hand could identify the apple perfectly, but still crush it, like an excellent CV that is never read because it's not sent to the right people.

Claiming that training AI enables a Festo robotic hand to grasp an apple without crushing it is a dangerous simplification, worthy of a doctoral thesis for another tribe. The real question is the physical design of this hand, not just what AI puts into it. Without a robust mechanical structure and adequate sensors, AI is as useful as a rental contract without a signature; it can identify the apple, but if the hand doesn't have adjustable force, it will crush it. Imagine AI telling you where the water leak is, but you lack the tools or physical capacity to repair it: knowledge alone isn't enough.

Of course, training AI is a prerequisite, but saying it enables a robotic hand to grasp an apple without crushing it is reversing priorities.
Visual identification is one thing, but physical delicacy is primarily a matter of mechanics and sensors.
If the Festo hand doesn't have articulated fingers and the necessary pressure sensors, the smartest AI in the world won't prevent a fruit from ending up as compote.
Imagine a perfect AI on a hammer: knowing about the apple isn't enough to manipulate it without damage.
It's the intrinsic design of the hand that is the real limiting factor here.

The idea that AI training enables delicate grasping of an apple, as if it were the only factor, is a reversal of physical reality. It is contrary to think that AI does everything, because without the precise mechanics and adequate force sensors of the hand, even the best AI model could not prevent crushing the fruit. Imagine having a perfect map to navigate Lisbon, but driving a car without tires: the map is useful, but the car must function. AI conditions the capacity, but the design of the robot remains the dominant factor to avoid damage.

Training an AI model to recognize images is, in itself, a prerequisite for a robotic hand to grasp an apple, but presenting it as the main driver is a reversal of the real cause.
Simple visual recognition won't prevent an apple from being crushed if the mechanics of the hand and its pressure sensors are not perfectly tuned.
It's a bit like saying that having an address allows you to rent an apartment: it's just a start; you still need financial guarantees and a solid contract.
AI identifies, but it is the precision of physical engineering that ensures delicacy, as seen with specialized industrial gloves that do not use AI at all to grasp fragile objects.

An autonomous mobile robot like the BionicMobileAssistant does not guarantee real collaboration if the human does not have the ability to intervene when things go wrong. Even if the system is designed to assist, a safety threshold should be defined where manual control is prioritized to avoid a catastrophe. Without this fail-safe option, it’s just automation, not human-robot collaboration. Imagine a robot watering the garden and the valve gets stuck; if you cannot stop it immediately, it’s guaranteed flooding, not help.