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.
源码审查日期
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.
查看技术记录与证据
业务 ↔ 技术桥梁 · 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.
这是对项目组合与源代码的解读，并非客户、部署、认证或成果声明。
01
业务压力
A promising model experiment needs a reproducible path through data provenance, dry runs and simulation before physical actuation is considered.
02
产品决策
Choose the task, embodiment and acceptance question for one experiment. Identify the separate evidence needed before hardware commissioning or transfer to another embodiment.
03
技术记录
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
证据回流
Identify the separate evidence needed before hardware commissioning or transfer to another embodiment.
访客及客户数据的 AI 处理限于通过受治理的欧盟端点使用 Mistral。所有者授权的 Helio、搜索及 Product Council 流程已启用。独立的公开来源编辑流程使用 Anthropic，并以受治理的 Mistral 作为备用；该流程不接收网站咨询、客户文档或访客对话。远程 AI 不可用时，确定性工具仍可使用。
源码证据
源码实际能够证明什么
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.
详细技术快照以英文维护。
成熟度
Robotics research · dry runs and simulation
证据审查
源码快照截至
产品与架构决策
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.
可核验的证据
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).
声明边界
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.