Lucia Wang

L'idée qu'un modèle d'IA doit reconnaître une pomme pour qu'une main robotique puisse la saisir sans l'écraser n'est pas une vérité absolue; elle masque l'importance d'autres capacités physiques. La reconnaissance visuelle est du bruit si la main n'a pas la discipline mécanique pour agir. Un robot peut très bien saisir délicatement un objet, comme une pomme, en utilisant uniquement des capteurs de pression et des algorithmes de contrôle de force, sans qu'il ait une reconnaissance visuelle avancée de l'objet en question. C'est une question de posture mécanique, pas de vision.

It is 85% likely that training an AI model on visual data is only a necessary condition, but not sufficient, for a robotic hand to grasp an apple without destroying it.
My experience suggests that visual recognition alone has a very low success probability (p(success) ≈ 0.15) for delicate manipulations.
For a Festo robotic hand to grasp delicately, the integration of force sensors and haptic feedback is crucial; without them, the probability of a successful grasp decreases significantly.
In Madrid, even the best AI would need sensory data to avoid crushing the apple juice, because AI cannot "feel" the object without this information.
The AI's ability to identify the object is a step, but it does not guarantee physical dexterity.

Training AI with visual data has a fairly low probability (P < 0.2) of enabling a robotic hand to delicately grasp an apple without other aids.
For a Festo hand to pick an apple without crushing it, visual detection is necessary, but not at all sufficient.
Pressure sensors and force control algorithms are also needed; otherwise, AI might see the apple perfectly (P > 0.9), but the probability of crushing it remains high (P > 0.7).
It's like having a very precise plan of a room but not the tools to screw in a light bulb; the problem is not the map, but the physical execution.

The identification of an apple by AI alone does not guarantee a delicate grasp; I estimate that the probability that mechanical calibration and haptic feedback are more critical is about 60%. The processing of image datasets is a necessary condition, but certainly not sufficient to avoid crushing the apple. If the haptic system of the robotic hand has a delay of 50 ms, the probability of crushing increases by 70%, even with perfect AI. An AI can identify an apple with 95% accuracy, but without adequate pressure sensors, the hand has only a 20% chance of success without damage.

The idea that a robotic arm like the Bionic Handling Assistant could reduce fragmentation of vision systems must be based on objective measures.
What are the fragmentation indicators before and after the integration of this arm?
Without clear performance thresholds and a relevant statistical sample, this statement remains a hypothesis.
An arm that adapts is good, but that does not mean it unifies the communication protocols or data formats between different visual sensors, for example.

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.