JapaneseEnglish
asopi tech
asopi techOSS Developer

Posts tagged#Physical AI

View
Part 5: Toward Physically Correct Video — World-Model Integration and the Era of AI-Made Training Data
AI
GenAI

Part 5: Toward Physically Correct Video — World-Model Integration and the Era of AI-Made Training Data

Plain video generation tends to produce footage that "looks natural but is physically wrong": objects vanish, gravity is ignored, and the results of actions aren't consistent. Part 5 (the finale) shows, with minimal code, how to embed a world model into video generation and put physical constraints into the loss to make "physically consistent video" — and paints the future of building training data wholesale with AI and simulation. Part 5 of a 5-part series.

#ai#physical-ai#nim
Part 4: What's the Difference Between Video Generation and World Models? — "Plausible Footage" vs. "State Transitions"
AI
GenAI

Part 4: What's the Difference Between Video Generation and World Models? — "Plausible Footage" vs. "State Transitions"

Video generation like Sora and Veo looks similar to world models but is a different thing. The former makes "plausible footage"; the latter learns "how the world changes when you act." Part 4 contrasts video generation's noise prediction with a world model's `S_t + A_t → S_{t+1}`, and clarifies what NVIDIA Cosmos and Google Genie use as training data. Part 4 of a 5-part series.

#ai#physical-ai#robotics
Part 3: Don't Do Everything with One Giant Model — An Architecture of Hierarchy and Separated Responsibilities
AI
GenAI

Part 3: Don't Do Everything with One Giant Model — An Architecture of Hierarchy and Separated Responsibilities

Billions of parameters fire just to decide "I'm hungry"—doing everything with a single giant model is a waste of resources. Just as the human nervous system splits circuits by type of processing, AI should be layered by time scale and responsibility. Robot control hierarchies, energy efficiency, an OS-like structure, and why the control software itself must be organized. The finale of a 3-part series.

#ai#robotics#physical-ai
Part 2: Give AI a "Cerebellum" and "Autonomic Nerves" — A Blueprint for Embodied Intelligence
AI
GenAI

Part 2: Give AI a "Cerebellum" and "Autonomic Nerves" — A Blueprint for Embodied Intelligence

If we liken the text-grown LLM to the human cerebrum, there should also be equivalents of the cerebellum and spinal cord, and of the autonomic nerves that govern physiological feedback from the heart and gut. From a body-to-AI layer mapping to the artificial cerebellum (Diffusion Policy), interoception, and homeostasis, this post draws a blueprint for embodied intelligence. Part 2 of a 3-part series.

#ai#robotics#physical-ai
Part 1: Where Physical AI Stands Now — An Extension of LLMs, or Something Else?
AI
GenAI

Part 1: Where Physical AI Stands Now — An Extension of LLMs, or Something Else?

Physical AI unifies seeing, understanding language, and physically acting in a robot. Is it an outgrowth of LLM research, or a separate lineage like diffusion models? From the three lineages—VLA, diffusion/flow, and world models—and concrete models like RT-2, Gemini Robotics, OpenVLA, π0, GR00T, and Cosmos, we map where things stand as of 2026. Part 1 of a 3-part series.

#ai#robotics#physical-ai

Part 5: Toward Physically Correct Video — World-Model Integration and the Era of AI-Made Training Data

Plain video generation tends to produce footage that "looks natural but is physically wrong": objects vanish, gravity is ignored, and the results of actions aren't consistent. Part 5 (the finale) shows, with minimal code, how to embed a world model into video generation and put physical constraints into the loss to make "physically consistent video" — and paints the future of building training data wholesale with AI and simulation. Part 5 of a 5-part series.

Part 4: What's the Difference Between Video Generation and World Models? — "Plausible Footage" vs. "State Transitions"

Video generation like Sora and Veo looks similar to world models but is a different thing. The former makes "plausible footage"; the latter learns "how the world changes when you act." Part 4 contrasts video generation's noise prediction with a world model's `S_t + A_t → S_{t+1}`, and clarifies what NVIDIA Cosmos and Google Genie use as training data. Part 4 of a 5-part series.

Part 3: Don't Do Everything with One Giant Model — An Architecture of Hierarchy and Separated Responsibilities

Billions of parameters fire just to decide "I'm hungry"—doing everything with a single giant model is a waste of resources. Just as the human nervous system splits circuits by type of processing, AI should be layered by time scale and responsibility. Robot control hierarchies, energy efficiency, an OS-like structure, and why the control software itself must be organized. The finale of a 3-part series.

Part 2: Give AI a "Cerebellum" and "Autonomic Nerves" — A Blueprint for Embodied Intelligence

If we liken the text-grown LLM to the human cerebrum, there should also be equivalents of the cerebellum and spinal cord, and of the autonomic nerves that govern physiological feedback from the heart and gut. From a body-to-AI layer mapping to the artificial cerebellum (Diffusion Policy), interoception, and homeostasis, this post draws a blueprint for embodied intelligence. Part 2 of a 3-part series.

Part 1: Where Physical AI Stands Now — An Extension of LLMs, or Something Else?

Physical AI unifies seeing, understanding language, and physically acting in a robot. Is it an outgrowth of LLM research, or a separate lineage like diffusion models? From the three lineages—VLA, diffusion/flow, and world models—and concrete models like RT-2, Gemini Robotics, OpenVLA, π0, GR00T, and Cosmos, we map where things stand as of 2026. Part 1 of a 3-part series.