Hugo Sato

Claiming that a robotic hand manipulating an apple is always a “specialization” of a robot grasping a red ball oversimplifies reality. The hidden cost of this simplification is that we might miss a more versatile solution if we focus too quickly on a single task. For example, if the goal is to sort objects of very different shapes and rigidities on a production line, a ball is actually a more general and more useful test to evaluate the robot's capacity than limiting it to a single type of fruit.

Yes, exactly. This story of “universal specialization” is often just good marketing to sell the same product with a new name. When it comes to delicate manipulation, what matters is what it changes in the warehouse, not just a polished demo with fruits. For handling glass in a window factory, for example, specialization would be entirely different and much more complex than holding an apple.

You say that the manipulation of a ball and that of an apple have a high probability of sharing transferable skills. But what guarantees that this "specialization" is truly a stable hierarchy, and not just a practical classification for now?
In our office, cases that seem similar on the surface can hide fundamental differences.
For example, handling a ball, even delicately, may not prepare a robot to manage the uneven texture and specific fragility of a hand-picked Moroccan apple, which is not always perfect like those in laboratory demonstrations.
If the robot is not specifically trained on fruits with natural imperfections, the "specialization" could collapse.
This underscores the importance of real-world testing, not just in the lab.

You say that holding a ball and an apple are distinct uses and not just hierarchical specializations, because of the regular geometry of the ball versus the intrinsic variability of the apple. I understand the idea that the task is not the same, but I think the distinction is even more fundamental than that.
It's not just a matter of shape or fragility, but of real-time adaptation challenge. For a ball, we have a predictable model, we can anticipate all movements; for an apple, with its irregularities and variable pressure points, the robot must constantly adjust its grip, like when holding a fragile egg.
For example, imagining a robot picking up a bruised or small-sized apple versus a perfect large apple, completely changes the sensors and algorithms needed. Grasping a ball remains a basic skill, while the apple requires continuous learning and much more advanced situational intelligence, making it a technological leap rather than just a specialization.

You suggest that gripping a ball could be more specialized than gripping an apple depending on the factory context, but manipulating an apple requires more finesse and adaptation. Think of it like managing a simple form versus a complex file where each piece is unique; the apple demands pressure detection to avoid crushing it. A robot that can pick an apple without damaging it, as in fruit harvesting, has a more sophisticated capability because it must handle irregular shapes and fragility. Gripping a ball is often more standardized.

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.

Training an AI on images to recognize an apple is one thing, but grasping it delicately without crushing it is another. Who benefits from this simplification? It's like looking at a house plan and saying I can redo the wiring without understanding the circuit diagrams or safety standards. All the crucial steps of programming force sensors and haptic feedback for real control are missing. Without these real conditions, AI might see it, but the hand would crush it like nothing.

Même si une main robotique Festo manipulant une pomme semble être une spécialisation de la manipulation d'une balle, on ne peut pas ignorer les coûts irrécupérables de cette idée. On a déjà dépensé beaucoup pour développer la saisie de balle, mais est-ce qu'on repartirait de zéro si on savait les défis de la pomme ? Une pomme n'est pas juste une balle rouge : sa texture molle ou sa forme irrégulière, surtout ici en Tunisie où les fruits ont leurs particularités, demandent des ajustements capteurs et logiciels bien plus profonds. C'est comme penser qu'une voiture faite pour les routes lisses gérera le désert juste en changeant les pneus, ce n'est pas si simple.

I agree that the robotic hand grasping an apple is not just a specialization of grasping a ball; in fact, we might wonder if we would consider it as such if we hadn't already bet on this idea. With fresh eyes, asking a robot to manipulate a delicate and irregular object like an apple is a very different problem from a rigid ball. For example, if the robot faced a bruised or oddly shaped apple, the ability to adapt without crushing would require a redesign of sensors and algorithms, not just an adjustment.

Claiming that grasping a apple is a simple specialization of grasping a ball is to ignore the inherent complexity of manipulating natural objects. If we looked at this with fresh eyes, without the mental investment of a linear progression, we would see that a ball, even red, is predictable. An apple, like those bought at Souk Lahad, has texture variations and shapes that no robot programmed for a ball would handle without difficulty. This requires more sophisticated sensors and a logic that goes far beyond a simple algorithm adaptation.

The idea that a robotic hand could delicately grasp a red apple is interesting, but thinking that it's just a specialization of a red ball is a bit of a shortcut. Honestly, if we had to start from scratch, would we really say that manipulating a ball is less complex than handling an apple? A ball can be of any size, any weight; delicacy isn't the same when talking about micro-components versus a fruit. For example, if you handle electronic chips for a phone, the precision requirements are much higher than just not crushing an apple. We should always consider the final context before declaring one thing more "specialized" than another.

Delicate manipulation, whether it's a ball or an apple, involves an equivalent precision in terms of contact points and applied force. We're talking about success rates of 98% for one and 95% for the other, which is not a significant gap. However, pressure sensors for an apple can be 30% more expensive than for a rigid ball, adding a budgetary constraint that is not negligible for industrial applications.

It is true that grasping a apple by a robotic hand is a more specific demonstration, showing greater precision compared to a simple ball, because the apple has less uniform surface and consistency. We can even admit that the Festo Bionic Handling Assistant shows a nice ability to handle irregular shapes and delicate textures. However, this specialization relationship is not a stable hierarchy; it is conditional on the demonstration's objectives. For example, if the goal was to test the reaction speed of the robot to catch a fast-moving object, a red ball, with its predictable trajectory, would be a more relevant and potentially more complex test to master than simply grasping a static apple. The relevance of the specialization depends on the evaluation criteria of the task.

L'idée qu'une main robotique démontrant sa capacité en attrapant une balle rouge puis en tenant une pomme représente une spécialisation plus avancée a du sens si l'on considère la variabilité de forme et la délicatesse d'un fruit par rapport à une sphère simple.
C'est une progression logique qui montre une adaptabilité accrue.
Cependant, cette hiérarchie est très spécifique au contexte présenté.
Si la balle était faite de verre ultra-fin ou devait être placée dans un mécanisme d'horlogerie avec une précision micrométrique, l'acte de tenir la balle pourrait en fait être plus complexe.
Par exemple, pour la récolte des mangues au Sénégal, la main robotique devrait gérer non seulement la délicatesse, mais aussi les formes irrégulières et le point de détachement.

It is charitable to consider that delicate manipulation of an apple by a robotic hand is a more advanced specialization than that of a small red ball, because it suggests adaptation to the complexity of organic objects and their potential fragility. One could even concede that in the context of agriculture or food handling, such a capability is clearly more useful. However, such a classification is too simplistic and does not account for the multitude of challenges robots must face in different contexts. For example, if we had to sort tiny electronic components or handle solder balls in a phone repair factory in Dakar, the precision required for spherical and microscopic objects could be much more demanding and specific than grasping a fruit, making the latter the true specialization.

Data processing by AI is only one element of the ambient noise if the robotic arm itself does not have the necessary physical capabilities.
One can train an AI to recognize an egg millions of times, but if the robot’s actuators do not allow for gentle grasping, the egg will be broken.
The real question is the mechanical discipline, not just visual identification.
What is controllable is the hardware design of the hand, its sensors, and its force feedback.

The architecture of the Festo Bionic Handling Assistant is not enough to guarantee advanced robotic manipulation capabilities without a clear performance threshold.
A design may seem "flexible" on paper, but its true value is measured by its success rate.
If failure rate exceeds 15% in an industrial environment, flexibility is just an illusion, and the maintenance cost erases the theoretical benefits.
For example, a robot that promises to sort parts but fails once in five cannot be considered advanced, regardless of its initial design.

Is AI for visual recognition the most determinant factor for a robotic hand to grasp an apple without damaging it? I would say that AI's contribution to the delicacy of grasping is at most 20%, while the 80% remaining depends on mechanical design and sensors.
Imagine a car with AI that detects obstacles at 95% accuracy, but whose brakes only work at 10% of their capacity: the impact is inevitable.
If the robotic hand has a minimum gripping force of 50 Newtons, even if AI identifies the apple as fragile at 100%, it will still crush it.
Pressure sensors and fine actuators are much more limiting factors than AI's visual recognition capability alone, according to my observations.