Experience is no longer local.

[ What We Build ]

We’re building the collective memory layer that allows humanoid machines to share real world experience.
Our system combines robotics, simulation, and decentralized infrastructure to turn physical skills into intelligence that can scale.

[ARCHITECTURE]

Building the infrastructure for collective robotic intelligence

These are the core architectural components of Ginkai.Each system represents a functional layer that enables robotsto learn, verify, and share skills at network scale.

[ARCHITECTURE]

Building the infrastructure for collective robotic intelligence

These are the core architectural components of Ginkai.Each system represents a functional layer that enables robotsto learn, verify, and share skills at network scale.

man playing tennis

e2s-pipeline

e2s-pipeline

man playing tennis

e2s-pipeline

women posing

universal-tensor-protocol

universal-tensor-protocol

women posing

universal-tensor-protocol

man in red outfit and har

proof-of-learning

proof-of-learning

man in red outfit and har

proof-of-learning

View Architecture

View Architecture

[ CORE MODULES ]

Ginkai is built as a modular intelligence layer. Each component plays a role in turning individual experience into collective robotic learning.

View Architecture

View Architecture

Experience to Skill

Capture real-world robot behavior and transform it into reusable, network-wide intelligence.

Universal Tensor Protocol

Proof of Learning

Robot Identity

Skill Distribution

[ LEARNING FLOW ]

[ 01

Observe

Robots interact with the physical world and collect real execution data. Failures and successes are captured as experience.

[ 02

Normalize

Raw telemetry is transformed into a unified motion representation. Experience becomes interpretable across different robot bodies.

[ 03

Verify

Learning is validated through decentralized simulation and consensus. Unsafe or invalid behaviors are filtered out.

[ 04

Share

Verified skills are distributed across the network. One robot learns. The system updates.

[ NETWORK LEARNING ]

A decentralized learning layer for humanoid robotics

Ginkai enables robots to learn collectively.Physical experience captured by one unit is transformed intoverifiable knowledge that strengthens the entire network.

[ NETWORK LEARNING ]

A decentralized learning layer for humanoid robotics

Ginkai enables robots to learn collectively.Physical experience captured by one unit is transformed intoverifiable knowledge that strengthens the entire network.

black women posing

Collective Learning

A single robot’s real-world experience can now become shared intelligence. Failures are no longer isolated. Lessons propagate across the network.

Global
Robot Growth

Global
Robot Growth

Humanoid robots are increasingly deployed across real-world environments.

2021

2022

2023

2024

2025

black women posing

Cross-Morphology Learning

Different robots use different hardware, sensors, and kinematics. Through normalized motion representation, learned behaviors remain executable across heterogeneous humanoid platforms.

Global
Deployment

Global
Deployment

Robotics adoption is expanding across regions and industries worldwide.

[ faqs ]

Understanding the Ginkai protocol

man

John Seleman

Protocol architecture, learning systems, and network safety. If you have a technical question, this is the right place to start.

X

X

Prefer async communication?

hello@ginkai.xyz

What does Ginkai actually do?

Ginkai enables humanoid robots to share real-world learning. A physical experience captured by one robot can be verified, tokenized, and safely reused by others across the network.

How is this different from centralized robot training?

Traditional systems silo data within a single manufacturer. Ginkai introduces a decentralized learning layer where experience becomes a transferable, verifiable asset.

How does Ginkai handle different robot bodies and hardware?

Through the Universal Tensor Protocol (UTP), robot-specific telemetry is normalized into a shared vector space. This allows skills learned on one morphology to be adapted to another.

How is safety verified before skills are shared?

All skills pass through a Proof-of-Learning (PoL) process. They are validated in large-scale digital twin simulations before being made available to the network.

Does Ginkai expose private or sensitive data?

No. Raw sensor data is processed locally. Only cryptographic proofs of learning are shared using zero-knowledge verification mechanisms.

Who can participate in the Ginkai network?

Humanoid robots, simulation contributors, and validators can all participate depending on their role in the ecosystem. Access is governed by identity and reputation.

[ faqs ]

Understanding the Ginkai protocol

man

John Seleman

Protocol architecture, learning systems, and network safety. If you have a technical question, this is the right place to start.

X

X

Prefer async communication?

hello@ginkai.xyz

What does Ginkai actually do?

Ginkai enables humanoid robots to share real-world learning. A physical experience captured by one robot can be verified, tokenized, and safely reused by others across the network.

How is this different from centralized robot training?

Traditional systems silo data within a single manufacturer. Ginkai introduces a decentralized learning layer where experience becomes a transferable, verifiable asset.

How does Ginkai handle different robot bodies and hardware?

Through the Universal Tensor Protocol (UTP), robot-specific telemetry is normalized into a shared vector space. This allows skills learned on one morphology to be adapted to another.

How is safety verified before skills are shared?

All skills pass through a Proof-of-Learning (PoL) process. They are validated in large-scale digital twin simulations before being made available to the network.

Does Ginkai expose private or sensitive data?

No. Raw sensor data is processed locally. Only cryptographic proofs of learning are shared using zero-knowledge verification mechanisms.

Who can participate in the Ginkai network?

Humanoid robots, simulation contributors, and validators can all participate depending on their role in the ecosystem. Access is governed by identity and reputation.

[ faqs ]

Understanding the Ginkai protocol

man

John Seleman

Protocol architecture, learning systems, and network safety. If you have a technical question, this is the right place to start.

X

X

Prefer async communication?

hello@ginkai.xyz

What does Ginkai actually do?

Ginkai enables humanoid robots to share real-world learning. A physical experience captured by one robot can be verified, tokenized, and safely reused by others across the network.

How is this different from centralized robot training?

Traditional systems silo data within a single manufacturer. Ginkai introduces a decentralized learning layer where experience becomes a transferable, verifiable asset.

How does Ginkai handle different robot bodies and hardware?

Through the Universal Tensor Protocol (UTP), robot-specific telemetry is normalized into a shared vector space. This allows skills learned on one morphology to be adapted to another.

How is safety verified before skills are shared?

All skills pass through a Proof-of-Learning (PoL) process. They are validated in large-scale digital twin simulations before being made available to the network.

Does Ginkai expose private or sensitive data?

No. Raw sensor data is processed locally. Only cryptographic proofs of learning are shared using zero-knowledge verification mechanisms.

Who can participate in the Ginkai network?

Humanoid robots, simulation contributors, and validators can all participate depending on their role in the ecosystem. Access is governed by identity and reputation.