Priya Muller

It is true that the demonstration with the red ball provides a foundation, but claiming that it directly specializes into a robotic hand grasping a apple without crushing it masks the real complexity. It is not a simple hierarchical specialization like moving from a general form to a specific sub-form. An apple has an irregular shape and a delicate surface that require much more advanced technical adaptations, such as precise pressure sensors to avoid damaging it, which is very different from a rigid ball. For example, grasping a plastic ball is one thing, but picking a ripe tomato without marking it is a whole other matter, requiring fine intelligence for each grip.

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.

I see well that you say that the Festo, even if it is flexible, does not solve the fragmentation of robotic systems because the software remains the weak point. That's exactly it, especially when you talk about the system that "crashes" due to poor lighting or an object moving too quickly, like at the post office. That's very well observed, it happens all the time.

We recognize that the dexterity shown with the ball is a useful basic ability, but the transition to manipulating an apple is not a simple specialization. There is about a 60% chance that the grasping requirements for an apple, with its irregular shape and fragile skin, are more complex than those of a uniform ball. For example, a robotic claw that grips well a ball may not necessarily pick a Gala apple without damaging it if it is not specifically calibrated for its texture. The idea that this hierarchical progression is stable is therefore probably too optimistic without sensor and algorithm adjustments.