Physical AI Blueprint family

Physical AI Blueprint family

Overview

Generative AI developed in the 2020s possesses the ability to "create" content, enabling anyone to utilize AI through natural language. Furthermore, recent generative AI has begun to exhibit logical thinking and reasoning capabilities, advancing to a stage where it simulates more abstract thought processes. AI agents that understand objectives, formulate plans, autonomously master tools, and execute tasks are now emerging. Beyond this, the emergence of Physical AI—a fusion of general-purpose artificial intelligence like AI agents and robotics—is anticipated, heralding a paradigm shift for humanity. The most significant change will be AI acquiring a physical body through robots and sensors, enabling it to autonomously accumulate "experience" in the physical world and gain new knowledge. This is expected to lead to the emergence of general-purpose robots capable of understanding human verbal instructions and acting autonomously based on their own judgments, even in unfamiliar environments. Such robots hold the potential for AI to become a partner for humans not only in cyberspace but also in physical space, significantly transforming our lives.

Within this Blueprint Family, we will release software stacks for realizing sensor networks for robots and edge AI, essential for achieving Physical AI.

 

Family Template

Case Attributes

Description

Informational

Type

New

 

Blueprint Family - Proposed   Name

Physical AI Blueprint family

 

Use Case

Robotics for restaurant and ready-to-eat industry

Robotics for agricultural, forestry, and fishing industries

 

Blueprint proposed

Robot basic architecture based on Sensor-rich soft end-effector system (SSES)

 

Initial POD Cost (capex)

$50K/one robot hardware

 

Scale

Expandable to automation in pharmaceutical, garment and textile, and services industries

 

Applications

Robots control elastic and non-uniform object under variable circumstance

 

Power Restrictions

Approx 500~1500W depending on configuration (mobility, number of arms, performance of onboard AI models)

 

Preferred Infrastructure   orchestration

Robot App: ROS2, Node-Red, Python, MQTT、processing、PLC

OS:Ubuntu


In the future, automatic calibration (using GPS signals), including measurement equipment, etc.

 

Additional Details

NA

 

 

Blueprints in this Family

Blueprint

PTL

TA Family Coordinator Nominee (Y/N)

Blueprint

PTL

TA Family Coordinator Nominee (Y/N)

Robot basic architecture based on SSES

 

 

Autonomous Agents Networks

 

 

 

Proposal Presentation