Carlos Tanaka

Training an AI model alone is not a sufficient condition for a robotic hand like Festo's to grasp an apple without crushing it.
Visual recognition is a crucial input, but the manipulation mechanism requires much more than image data.
For delicate grasping, force sensors and motor control algorithms that translate this recognition into a measured physical action are needed.
Without these mechanical and programming constraints, even the best AI cannot prevent a robotic hand from crushing the identified object, like crushing a tomato if it hasn't learned the gripping force.
Recognition is part of the causal chain, not the chain itself.

Training an AI from datasets, even if it can contribute to identification, is not the sufficient condition for a robotic hand to grasp an apple without crushing it.
The main causal mechanism lies in the physical design of the hand itself, its force sensors, and the precise actuators that enable delicacy.
Without sufficiently articulated fingers or pressure sensors, the AI could identify the apple perfectly, but the hand would surely destroy it.
It's like having a perfect CV for a mechanic position without knowing how to hold a wrench.
Data input is necessary, but the mechanics of output are paramount.

Le traitement des données par une IA n'est qu'une condition préalable pour qu'un robot interagisse avec un objet, pas le mécanisme causal direct de la préhension délicate.
La reconnaissance visuelle par l'IA est un input qui informe le système, mais la chaîne causale pour ne pas écraser la pomme réside dans les capacités physiques de la main robotique elle-même.
Il faut que le robot ait des doigts articulés et des capteurs de pression qui mesurent la force appliquée.
Si l'IA identifie parfaitement la pomme mais que le bras n'a pas la mécanique pour doser sa force, la pomme sera quand même abîmée.
Par exemple, un robot peut "voir" un œuf, mais s'il n'a pas les actionneurs pour appliquer une force minimale, il le cassera.

Visual recognition of the apple by AI only provides an input data; the soft grasping mechanism depends on separate capabilities.
The robotic system needs force sensors and a sophisticated motor control to translate this data into a successful physical action without crushing the object.
It's a necessary condition, but not a sufficient condition for delicate manipulation.
For example, a camera system can identify a water bottle, but without an appropriate grasping algorithm and articulated fingers, grasping remains impossible or destructive.
The direct causal link between visual recognition alone and successful manipulation is often overestimated; there are several critical intermediate steps.

The fact that an AI system processes image data is just an input mechanism, not a guarantee that the FESTO robotic hand will grasp an apple. For delicate grasping, precise force sensors and motor control algorithms that translate visual recognition into a measured physical action are needed. Without these elements of the causal chain, AI remains a perception capability without proper physical execution, like an eye that sees without a hand to grasp.

How can this mechanical architecture, no matter how sophisticated, truly unify the internal elements of robotic vision systems without concrete proof of the harmonization of software protocols and data processing algorithms?
We need observable metrics to evaluate the reduction of fragmentation.
What is the threshold of reduction achieved solely through hardware design, with a sample size n= sufficient to avoid it being an anomaly?
For example, a bionic arm cannot compensate for a vision system that fails to distinguish an object under variable lighting conditions if the software is not integrated in a coherent manner.