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.
Source reviewed
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.
Explore the technical record and evidence
Business ↔ technology bridge · 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.
This is a portfolio and source interpretation—not a customer, deployment, certification, or outcome claim.
01
Business pressure
A promising model experiment needs a reproducible path through data provenance, dry runs and simulation before physical actuation is considered.
02
Product decision
Choose the task, embodiment and acceptance question for one experiment. Identify the separate evidence needed before hardware commissioning or transfer to another embodiment.
03
Technical record
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
Evidence return
Identify the separate evidence needed before hardware commissioning or transfer to another embodiment.
Repository/runtime boundary
This page interprets source from a separate venture repository. Source presence does not make the venture a service running inside hyperion-consulting.io.
Visitor and client-data AI processing is restricted to Mistral through the governed EU endpoint. Owner-authorized Helio, Search and Product Council paths are enabled. A separate public-source editorial lane uses Anthropic with governed Mistral backup; it receives no website enquiries, client documents or visitor conversations. Deterministic tools remain available when remote AI is unavailable.
Source evidence
What the source actually establishes
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.
Maturity
Robotics research · dry runs and simulation
Evidence review
Source snapshot through
Architecture and product decisions
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.
Inspectable evidence
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).
Claim boundary
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.
Repository evidence is not client, deployment, certification, or outcome evidence.
Work with Hyperion
Frame a robotics experiment
An experiment brief with provenance requirements, simulation criteria and a separate physical-validation gate.
Bring a short description of your situation, constraints and decision. We agree the scope, responsibilities and quote before work begins.