Les synthèses commerciales sont disponibles dans cette langue. Les parcours produit et les preuves techniques sont maintenus en anglais.
IA physique et robotique
Hyperion Robot Learning Lab
Relier la recherche en apprentissage robotique à un parcours d’évaluation contrôlé.
Une expérimentation prometteuse sur un modèle nécessite un parcours reproductible : provenance des données, essais sans action physique et simulation, avant d’envisager une commande physique.
Recherche robotique · essais sans action physique et simulation
An experiment manifest
Task
One bounded robotics task
Provenance
Dataset, model and embodiment recorded
Environment
Dry run or simulation
Next gate
Review failures before physical qualification
Decision logic and control flow
Explore the reasoning
Choose the task, embodiment and acceptance question for one experiment.
Engage now
An experiment brief with provenance requirements, simulation criteria and a separate physical-validation gate.
Inspect
Robotics research · dry runs and simulation
Hyperion Robot Learning Lab dry-run, simulation, provenance and guarded-deployment paths (source through 22 October 2026).
Still to validate
No physical commissioning or learned-policy success on real robots is established.
Scope a reproducible robotics experiment. Physical commissioning and learned-policy success on real robots are not established by these records.
Sources examinées
The decision behind the system
What must be proven before a robot experiment can act?
A robotics research programme joining provenance, dry runs, simulation and guarded deployment work. Related model and runtime experiments are presented as one research family.
This research family concerns learning and runtime experiments. Auralink develops the machine-platform design; the portfolio does not establish that all research components are integrated.
Evaluation before actuation
Reachy and SO-101 research paths use dry-run and simulation boundaries, with provenance and deployment controls kept explicit.
Phytral
A dual-system vision-language-action research prototype explores perception and action. Large-scale training and real-robot validation remain future work.
Runtime and distributed research
The Physical AI Stack explores local model/runtime catalogues and rollback. Embodied Intelligence Mesh explores distributed training, validation and inference in simulation.
Read the complete system walkthrough
Define
Choose the task, embodiment and acceptance question for one experiment.
Trace
Record data and model provenance, configuration and the runtime boundary.
Exercise
Inspect a dry run or simulation, including failures and assumptions about the physical environment.
Qualify
Identify the separate evidence needed before hardware commissioning or transfer to another embodiment.
Inspect the source, test conditions and operating limits in the technical record.
Explorer le dossier technique et les preuves
Pont métier ↔ technologie · Physical AI capability experiment
What must be proven before a robot experiment can act?
This research family concerns learning and runtime experiments. Auralink develops the machine-platform design; the portfolio does not establish that all research components are integrated.
Il s'agit d'une interprétation du portefeuille et des sources, et non d'une affirmation de client, déploiement, certification ou résultat.
01
Pression métier
A promising model experiment needs a reproducible path through data provenance, dry runs and simulation before physical actuation is considered.
02
Décision produit
Choose the task, embodiment and acceptance question for one experiment. Identify the separate evidence needed before hardware commissioning or transfer to another embodiment.
03
Dossier technique
Reachy and SO-101 research paths use dry-run and simulation boundaries, with provenance and deployment controls kept explicit. A dual-system vision-language-action research prototype explores perception and action. Large-scale training and real-robot validation remain future work. The Physical AI Stack explores local model/runtime catalogues and rollback. Embodied Intelligence Mesh explores distributed training, validation and inference in simulation.
04
Retour des preuves
Identify the separate evidence needed before hardware commissioning or transfer to another embodiment.
Frontière dépôt/exécution
Cette page interprète les sources d'un dépôt de venture distinct. Leur présence ne signifie pas que la venture est un service exécuté dans hyperion-consulting.io.
Le traitement IA des données des visiteurs et clients est limité à Mistral via le point de terminaison européen gouverné. Les parcours Helio, Recherche et Product Council autorisés par le propriétaire sont activés. Un circuit éditorial distinct de sources publiques utilise Anthropic avec Mistral gouverné en secours ; il ne reçoit ni demandes du site, ni documents clients, ni conversations des visiteurs. Les outils déterministes restent disponibles sans IA distante.
Preuves issues du code source
Ce que le code source permet réellement d’établir
A robotics research family joining the Hyperion Robot Learning Lab integration record with related Phytral, Physical AI Stack and Embodied Intelligence Mesh experiments. Each component retains its own source and validation boundary.
Le relevé technique détaillé est maintenu en anglais.
Maturité
Robotics research · dry runs and simulation
Revue des preuves
État du code au
Décisions produit et architecture
Keep experiment provenance separate from claims of physical robot success.
Use dry-run and simulation evidence before seeking physical integration authority.
Treat model, runtime and distributed-system experiments as components of one research programme.
Éléments vérifiables
Hyperion Robot Learning Lab dry-run, simulation, provenance and guarded-deployment paths (source through 22 October 2026).
Phytral dual-system vision-language-action prototype (1 June 2026) and Physical AI Stack host/runtime/model catalogue work (1 September 2026).
Embodied Intelligence Mesh simulated training, validation and inference vertical slice (25 June 2026).
Limites des affirmations
No physical commissioning or learned-policy success on real robots is established.
Large-scale model training and production GPU, ROS2, industrial and hardware adapters require separate validation.
Component source does not establish interoperability, safety certification or a completed integrated product.
Les preuves du dépôt ne constituent pas des preuves client, de déploiement, de certification ou de résultats.
Travailler avec Hyperion
Cadrer une expérimentation robotique
Un cadrage expérimental précisant les exigences de provenance, les critères de simulation et une étape distincte de validation physique.
Apportez une brève description de votre situation, de vos contraintes et de la décision à prendre. Le périmètre, les responsabilités et le devis sont convenus avant le début des travaux.