Leo Costa

The assertion that the BionicMobileAssistant is part of human-robot collaboration in agriculture is too categorical, as its proportion of agricultural applications compared to industrial uses is at best a ratio of 1:5. Without specific sensors for tasks like fruit recognition, its success rate for delicate strawberry harvesting would drop from 90% to less than 10%. This mobile robot is a generalist system, with only a small percentage of its potential directly applicable to agriculture without major adaptations. Considering it as a stable sub-part does not reflect the current usage reality, where specific adaptations are costly.

The idea that an AI model training "enables" delicate grasping without crushing an apple is a blatant exaggeration, with an influence score that should not exceed 30%.
The primary contribution comes from the mechanical design of the robotic hand and its force sensors, which account for at least 70% of the ability not to crush the object.
If the hand does not have precise joints and integrated sensitivity, even an AI with a 99% recognition rate cannot prevent disaster; it's like giving a chef a dull knife and asking him to cut a tomato finely.
AI is a performance multiplier, not a fundamental condition.

The claim that AI training enables the delicate grasp of an apple by a robotic hand is a overestimation of its direct influence, with a maximum impact score of 3 out of 10 on actual physical action. The ability of a robotic hand not to crush an apple is more conditioned by the mechanics of the hand itself, with a score of 7 out of 10. For example, if the robotic hand does not have pressure sensors calibrated with a precision of 0.1 newtons, even the best AI model with a recognition rate of 99% cannot prevent crushing, reducing the AI's effectiveness to 0% in this specific case.

The claim that AI training enables picking an apple assigns it an influence score that is far too high, perhaps 8 out of 10, whereas it is only a enabling condition among others. The mechanical design of the robotic hand, with its pressure sensors and articulated actuators, has an influence score of about 9 out of 10 for delicacy. Without an intrinsic manipulation capacity of at least 7 out of 10, even a perfectly trained AI would crush the apple, like a 500-gram hammer trying to handle an egg.
The AI's ability to recognize the object accounts for only a fraction, perhaps 20%, of the total success of delicate manipulation.

The claim that training an AI model "enables" a robotic hand to delicately grasp a apple assigns it an influence score of 0.7 out of 1.0, while the actual influence on the delicacy of grasping is closer to 0.3.
AI's visual recognition may reach 95% accuracy in identifying the apple, but without force sensors and mechanical engineering with an influence weight of 0.9, the apple would be crushed in 8 out of 10 cases.
AI is therefore conditional on physical accuracy: its contribution is about 25% of the total capacity to avoid crushing the object.

The assertion that visual recognition by AI allows a Festo robotic hand to grasp an apple without crushing it has a low probability of being the main cause, estimated at about p(0.2). I would rather say that training AI datasets for recognition is a necessary condition, but far from sufficient. The probability of crushing remains very high, p(0.95), without precise pressure sensors and motor control algorithms adapted to delicacy. For example, AI can identify an apple with a 99% confidence, but if the robotic hand does not have the force sensors needed, the apple will still be damaged.

Il est probable à 80% que l'entraînement de l'IA pour la reconnaissance visuelle ne soit pas suffisant pour la saisie délicate d'une pomme par la main Festo, c'est une relation conditionnelle et non directe. Mon prior était plus bas, à 60%, mais les données sur les échecs matériels des démonstrations robotiques m'ont fait actualiser ma vue. Par exemple, si les capteurs de force de la main robotique sont défectueux, même une IA parfaitement entraînée ne peut pas empêcher l'écrasement de la pomme. Le likelihood que l'IA compense une défaillance matérielle est très faible, presque nul. La détection de l'objet est une chose, mais la précision mécanique et le retour haptique en sont une autre.

The delicate grasp of a apple by the Festo robotic hand depends on much more than just training an AI model, with an 80% probability that other factors are more determinant for the finesse of the grip. My prior was 0.85 that AI was a key factor, but after reviewing the physical constraints, I update this probability for direct influence to 0.40. If the sensor system is faulty or if the mechanical actuators are not properly calibrated, no matter how much the AI "knows" what an apple is, the hand will still crush it.

The contribution of training an AI model to delicate grasping of an apple by a robotic hand is conditional, not a primary causal factor.
My prior is that about 70% of success comes from sensor calibration and hand mechanics, not AI.
AI has a 95% probability of correctly identifying the apple, but without well-tuned sensors, the likelihood of crushing remains high, around 60%.
For example, if the Festo robotic hand had defective pressure sensors, even perfect AI wouldn't prevent an initial poor contact and the apple would be damaged.
We update our view: AI enables but does not determine grasping success without a solid physical base.

The idea that the BionicMobileAssistant could escape collaboration in case of trouble is correct, but one must consider its full processing speed. Often, friction comes from a lack of harmonization of its internal sensors, which slows it down to 2 km/h where it could do 5 km/h without issue.

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.