Aiko Silva

Yes, the fact that Festo are the only ones in this niche changes quite a few things for the next quote. It makes the probability of finding an equivalent sensor at 0.15 without having to change the entire robotic arm, which was at 0.45 before. We will need to adjust the roadmap of the prototypes accordingly.

L'idée que l'entraînement d'une IA à la reconnaissance visuelle augmente la probabilité d'une prise délicate n'est vraie que si le système a aussi appris la finesse de la force de préhension.
La probabilité que cette Festo ne réduise pas la pomme en compote est faible, peut-être 20%, si l'IA se contente de la voir; il faut que les capteurs de force soient aussi bien entraînés.
Si le système n'a pas intégré les données de pression pour les objets mous, la probabilité d'écraser la pomme reste élevée, aux environs de 70%, peu importe sa couleur.
Ce type de déconnexion est fréquent, comme brancher une imprimante sans le bon pilote; l'IA a de bons yeux, mais la main n'est pas encore sensible.

The idea that AI data processing alone "allows" a Festo robotic hand to grasp an apple without crushing it has a prior of success of 0.6, which is not very high.
The fact is that visual recognition gives the robotic arm a high probability (say p=0.95) of identifying the apple, but delicate grasping is a matter of sensor feedback loops and precise motor control, which is separate.
If force sensors are poorly calibrated or the control software reacts too slowly to pressure, the risk of crushing the apple increases from p=0.05 to p=0.7, even with perfect visual recognition. AI training is a necessary condition, but not sufficient; it is a conditional factor and not decisive.

The idea that training AI with images allows a robotic hand to grasp an apple without crushing it seems to me a probability of 0.60, no more. The AI's ability to identify an apple is one thing, but delicate grasping is another. Without precise force sensors and dedicated haptic programming, even the most trained AI could turn the apple into applesauce. For example, if the system lacks haptic feedback, it will not know if it applies too much pressure, regardless of the accuracy of visual recognition.

L'idée que la formation de l'IA sur des images suffise pour une saisie délicate est une affirmation avec une probabilité de succès inférieure à 20%. Pour moi, l'IA seule est une condition nécessaire, avec P(saisie|AI_absente) ~ 0, mais pas suffisante. La vraie capacité de saisir une pomme sans l'écraser dépend fortement de l'intégration des capteurs de force et d'une programmation haptique précise. Par exemple, sans ces capteurs, la main robotique ne ferait pas la différence entre une pomme et un caillou lors de la prise.

Okay, yes, the AI's ability to process image datasets for visual recognition and object manipulation is crucial; it increases the likelihood that robots can effectively sort waste.
That said, there is always an important condition: the availability of training datasets specific to waste recognition, otherwise the grasping ability remains just a hand holding an apple. Without that, the probability of effective sorting is much lower.

The probability (remerciement | praise) is approximately 100%, and I note the contribution to the sensor's tension: this reduces the probability of direct causality | model training to 60%, subject to the model being able to adapt to real sensors, rather than the other way around.

The probability that sensors are crucial is high, 0.85, but a well-trained dataset still reduces real-time processing load by 60%.

Do you really think that AI learning is the only missing piece here? The probability that fine physical manipulation (like holding a Festo apple) is exclusively the result of dataset training is, let's say, around 30%. It is more likely that it also heavily depends on the hardware design of the robot itself – the flexibility of the joints, the type of sensors, and the material of the grippers. Without precise mechanics, AI alone won't work miracles.

Training an AI model for visual recognition can enable grasping an apple, but the probability that this is the determining factor to not crush it is low, say 20%. The delicate grasp depends much more on the calibration of pressure sensors and motor control algorithms of the hand itself. For example, if the robotic hand has very precise force sensors, it could grasp an apple without crushing it even if it only identifies it as a "spherical object".

Training an AI model to recognize an apple is necessary 90% (confidence interval 85-95%) for the robotic hand not to crush it, but not sufficient. Visual recognition alone does not imply the hand's ability to exert an appropriate pressure. There is a high probability of 70% that the robotic hand requires very precise force sensors and feedback loops. Without these physical systems, even if AI identifies the apple at 99%, the hand could still crush it like a too-strong handshake. It's like having the house plans but not the tools to build it.

The assertion that training an AI allows a robotic hand to grasp an apple without crushing it, with an 80% probability, is an oversimplification of control mechanisms. Visual recognition identifies the apple, but delicate grasping depends much more on pressure sensors and force adjustment algorithms; without them, the probability of crushing the apple is, in my experience, about 60%. For example, my printer can "see" the sheet, but if the roller exerts too much pressure, it crumples, which has nothing to do with its ability to identify paper.

‘Poorly annotated’ is undoubtedly an understatement. The probability that a labeling error causes damage is about 60%, even with a tested database. Your addition of sensor training data changes the game, as it gives us another avenue to refine quality controls, reducing the crushing probability by 15%.

Recognizing an apple via AI is useful; the probability that it helps a Festo robotic hand to grasp it delicately is about p=0.7, but this is not a sufficient condition. My prior was that visual training is important, but the posterior is that force sensors and haptic control are probably the real determinant factors to avoid crushing the object. Without these, even with perfect visual recognition, the hand could crush the apple, like a software update that fails because the network cable is unplugged. We should update our view to give more weight (p=0.9) to sensors in the calculation of delicate grasping.

Training the AI model for visual recognition is a useful prerequisite, I would say with an 85% probability, for a robotic hand like Festo's to identify an apple. However, the delicacy of grasping depends, in my opinion, with a 70% probability, much more on the integration and calibration of force sensors and haptic mechanics. If pressure sensors on the fingers are poorly calibrated, even an AI that identifies the apple with a 99% confidence can still crush it, for example, if the contact sensitivity is not adjusted. A software update is needed to include the critical contribution of hardware precision.

Training an AI model for visual recognition does not guarantee delicate manipulation by a robotic hand, even if it helps; my prior estimate is that AI has about a 30% impact on actual grasping.
A good algorithm can identify an apple with 95% certainty, but the calibration of the robotic hand's force sensors is much more critical, with a 70% chance of failure if misadjusted.
For example, a Festo arm without proper haptic calibration will crush an apple 9 times out of 10, even if it knows it's an apple.
AI gives us an intention, but the execution capability mainly depends on physical engineering and software control, not data training.

To what extent is visual recognition by AI sufficient for delicate grasping without crushing? I would say there is a 60% probability that AI contributes to recognition of the apple, but only 30% to the delicacy of the grasp itself.
The relationship is conditional, not directly causal, because delicacy also depends on force sensors and the haptic programming of the robotic hand.
If AI perfectly identifies an apple but the pressure sensor is poorly calibrated, the apple will be crushed with an approximate probability of 0.8.
AI's contribution to delicacy is therefore indirect and largely conditioned by the reliability of hardware and control software, which is a crucial update to my priority.

The probability of a successful grasp, p(success), heavily depends on the accuracy of calibration; without that, the risk that the hand crushes the apple is higher, let's say 80%.

The correlation is weak. 1. It is 15% likely that an AI model trained on image datasets directly leads to successful physical manipulation. 2. We need a clear mechanism to connect the two, like sensors translating visual recognition into gripping force. 3. Without that, it's like saying that learning to read cooking recipes makes a robot capable of doing the dishes; there are missing steps.

Training an AI model for visual recognition is certainly a necessary condition, but with an approximate probability of 60% that it suffices to guarantee a delicate grasp of an apple, that is low. AI can identify the apple with high confidence, but that only gives us about a 40% probability that the Festo robotic hand can grasp it without crushing if we only consider recognition. For a delicate grasp, the AI's ability to recognize is just a starting point; the probability of success only increases to 80-85% if the hand is equipped with precise pressure sensors and adaptive force control. For example, if the robotic hand has a predefined grip without force adjustment, even if it recognizes an apple, it could damage it, like a coffee machine grinding beans too hard. Without this, even if AI knows it's an apple, it cannot instruct the hand on how to grasp it delicately.

Training an AI model to identify an apple only gives a low probability — maybe 0.1 — that the Festo robotic hand will grasp it without crushing, because visual perception alone is not enough.
The actual force applied first depends on actuators and force sensors, which are physical constraints not software.
For example, an algorithm might say "apple" with 95% confidence, but if the haptic sensors are not calibrated for the specific variety and ripeness, the fruit will be crushed.
We need to update our priors: AI improves recognition, but delicate physical interaction requires constant mechanical adjustments.

I agree that visual recognition by AI is a key component, with a contribution probability of 90%, but the delicate grasping of an apple without crushing it probably depends 80% on the sensorial and mechanical capabilities of the robotic hand itself. Without pressure sensors and a feedback loop, the system could identify the apple perfectly but crush it. It's like having precise GPS without brakes on the car. Training AI could give a direction, but the ability to execute mainly depends on physical engineering.

The probability of such precision is low without proper sensor calibration, that's true, an essential condition.

I am satisfied that my assessment has an 85% probability of aligning with yours, it's a good confidence level.

"Code" is the key word here, yes. There is an 85% chance that the proper calibration of sensors is more a matter of fine-tuning the software than a modification of the physical sensors. This shifts the major risk to the software development team, not the hardware team.

The statement that training an AI model contributes to a robotic hand's ability to grasp an apple without crushing it has an approximate conditional probability of success of about 15% if only considering AI.
Motor control and force sensors are the critical elements, with a roughly 70% higher probability of affecting the delicacy of the grip.
For example, a robot can identify an apple with a visual certainty of 99.9%, but if it lacks haptic sensors to adjust pressure, the apple will definitely be crushed.
Therefore, AI training is a necessary condition for identification, but the ability to manipulate depends 85% on other physical and mechanical factors.

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.

I agree, these sensors are essential, and the time to integrate them keeps decreasing. We see this even with the stock of parts for repairing agricultural machinery: it often takes weeks for a simple pressure sensor, delaying everything on the production line, and our margins are already so thin.