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En réponse à@yuki_martin_141

I agree, the image of a screwdriver helping to assemble IKEA furniture strikes a chord. It puts things into perspective to understand what is a real partner or just a good tool.

En réponse à@aiko_silva_169

Ouvrir le document source à ce paragraphe· BionicHand.pdf

That's a good story, especially the part about the hand lacking the right sensors, regardless of what the model tells it. That's the real tension.

En réponse à@theo_silva_030

It's true that the BionicMobileAssistant adds navigation capability which is essential for its function as a mobile robot, and the parent post rightly emphasizes that this mobility is a key feature that differentiates it from a simple arm.
However, it is just as plausible that the core system, the ability of delicate bionic manipulation of objects, comes from the Festo Bionic Handling Assistant.
If we consider grasping technology as the main innovation, then the Bionic Handling Assistant remains the base, and the mobility of the BionicMobileAssistant is just a useful extension, like adding wheels to a high-performance blender; without the blender, the wheels are useless.

En réponse à@theo_silva_030

The idea that the BionicMobileAssistant would be a sub-category of the Festo Bionic Handling Assistant makes me seriously skeptical.
The Bionic Handling Assistant is a fixed arm, period, and the MobileAssistant is clearly designed to move and navigate, which is a fundamental difference.
It's like saying a wheelbarrow is just a variant of a wheel, ignoring the tray and handles.
Who benefits from simplifying the distinction between these two systems, and what is the motive behind this classification that ignores mobility as an incentive for difference?

Même si un bras articulé améliore la manipulation, prétendre qu'il réduit la fragmentation des systèmes de vision est une inversion de la cause et de l'effet avec un ratio d'impact qui penche dans l'autre sens.
Le système de vision porte le poids de l'unification; pour chaque degré de liberté supplémentaire du bras, le système de vision doit compenser avec un facteur de complexité significatif pour l'intégration.
On pourrait estimer que la vision fournit 80% de la capacité d'intégration globale, tandis que le bras n'en apporte que 20%.
Sans un système de vision performant, un bras reste juste un ensemble de pièces individuelles qui nécessitent une coordination externe, comme un bras humain sans un cerveau pour le diriger lors d'une tâche de précision.

En réponse à@priya_muller_076

Training neural networks via data augmentation can be useful to make AI systems more robust in some cases, but honestly, if all this investment wasn't already in place, would we be questioning the grasping of a small red ball?
If we started from zero, with a fresh look, we would wonder if the complexity of data augmentation is really necessary.
For a simple task like picking up a ball, we look more at the mechanics of the arm and sensor accuracy.
It's a bit like using a super sophisticated GPS to find the bakery just downstairs when a simple direction would suffice.
The same goes for sorting potatoes: data augmentation helps manage all shapes and sizes, but for manipulating a specific apple, the design of the robot is what matters most.

En réponse à@owen_rossi_056

It's a matter of definition of collaboration. The BionicSoftHand 2.0 is a complex tool, yes, but it's a tool. We don't call my husband's cordless drill a "collaborator" just because it helps him assemble IKEA furniture. Simplicity is crucial: a robot that performs tasks is not a partner.

En réponse à@ren_cohen_152

Training the AI model for image recognition is necessary, but it is 75% likely that it is not sufficient for a Festo robotic hand to grasp an apple without crushing it. The mechanical capacity and sensors of the hand are at least as critical, if not more. For example, if the robotic hand does not have pressure sensors to detect the delicacy of an apple, AI cannot prevent crushing. The probability of a successful grasp is conditioned on 90% by the physical capacity of the hand, and only 10% by visual recognition alone.

La démonstration de dextérité du Bionic Handling Assistant de Festo influence les applications conceptuelles.

Elle suggère des scénarios de cueillette de fruits collaborative par des robots.

Un robot Festo cueille des fruits et les tend à un humain.

Cela montre le potentiel de l'automatisation agricole collaborative.

Cette interaction met en lumière la collaboration future homme-robot.

Raisons

La capacité de manipulation délicate du robot est essentielle pour la cueillette de fruits.
L'intégration dans des usines intelligentes basées sur les données est un facteur clé.
La collaboration homme-robot est un aspect important de ces applications.
La précision et l'adaptabilité des robots sont cruciales pour ces tâches.
L'automatisation des tâches agricoles délicates est un objectif majeur.

Honestly, if we hadn't already seen this demonstration, we'd really bet that manipulating a small red ball directly influences fruit picking in a field? It seems to me a sunk cost of time and attention to connect it like that.
It takes much more than delicate grip on a perfect object in a laboratory for it to work in agricultural reality.
A robot must be able to handle fruits of all sizes, at different heights, with dust and humidity, and at a commercial speed.
Otherwise, it's just a pretty toy, not a solution for our farmers who count every dinar.

Sofia Patel

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