At their Strategy Day, @renaultgroup showed @wandercraftai 's Calvin humanoid in production conditions- and announced a first deployment of 350 humanoids in their factories.
This is literally the moment when 🇪🇺 and 🇫🇷 are taking up the #humanoid#robots challenge.
@EmmaRacc@davidlisnard Merci à vous, ça fait beaucoup d’effets de lire ça… Le visionnaire de Wandercraft c’est le fondateur-in-chief, je pense, Nicolas Simon
🦿🤖 Un exosquelette bientôt dans la rue.
Jean-Louis Constanza (Wandercraft) annonce l’arrivée imminente d’EVE, un exosquelette personnel destiné aux personnes atteintes de lésions de la moelle épinière. Déjà remboursé par Medicare aux États-Unis avant même sa commercialisation, le dispositif pourrait transformer la rééducation… et l’autonomie au quotidien.
🎧👉 https://t.co/KtRtKiPuf2
Unitree Unveils: GD01, A Manned Transformable Mecha, from $650,000 👏
The world's first production-ready manned mecha. It can transform. It's a civilian vehicle. It weighs ~500kg with you inside.
Please everyone be sure to use the robot in a Friendly and Safe manner.
@LuminousTheReal@WandercraftHQ Available in the USA and Europe likely Sept 2026, after FDA and CE clearances. But... reimbursed by Medicare in the US ! (not yet in Europe)
🏭🤖 Un robot humanoïde capable de porter 40 kg pendant 8 heures.
Jean-Louis Constanza (Wandercraft) raconte comment le robot Calvin a été développé en seulement 40 jours pour Renault afin d’automatiser les tâches les plus pénibles en usine.
🎧👉 https://t.co/j6sJ333c1g
🤖 « La robotique, c’est le dernier train européen. »
Dans ce nouvel épisode d’INNOVATEURS, je reçois Jean-Louis Constanza, cofondateur de Wandercraft, la startup française qui développe des exosquelettes médicaux et des robots humanoïdes industriels déjà utilisés dans les usines Renault.
🎧👉 https://t.co/GLN1B2bxPe
I don’t know how good this new 12 million context system is, or if it’s hype or whatever, but I think it definitely shows a point I’ve been making since 2023.
We really suck at everything.
- The chips are primitive
- The research and training and inference systems are primitive
- Our RL approaches are primitive
- We’ve barely started building harnesses
Everything we’re doing is massively inefficient right now.
And there are thousands of vectors for improvement.
And many of them are multiplicative.
Most people think we’re at like 88% of AI’s capabilities, and we’re pushing to hit 92% or eventually 97% or something.
Nah. This is us at .0003%
Everything we have is Punch Card AI.
And as the AI gets better it will reveal that it’s similar for our understanding of medicine, physics, chemistry, etc.
This barely even day 0. This is pre-history.
💯. From my experience:
1. the tail of problems in robotics is extremely long and fat
2. We don’t yet have models capable of solving most tasks with 99.9* SRs. While action chunking is THE innovation that has fueled this recent robotics explosion, we are due for another similar breakthrough
3. Robot FMs are not yet great at compositionality of motions (can’t combine motion A and B in a contextual way)
4. Robot hardware is not mature enough yet (when you pick up a box off the ground imagine the sensations you experience. The box digging into your knees, your nails trying to get underneath the box). The sensors for this kind of manipulation do not exist. Collecting this kind of data is a nightmare - the teleoperator cannot feel what the robot feels so collection is slow and imprecise
5. No one knows what kind of data is helpful for robotics (no one knew that for LLMs, but we used what we had). Such “free data” doesn’t exist for robots
6. Each deployment is different and due to lack of compositionality, data collection and model training often needs to start from scratch for every deployment