A Tutorial on Physical AI from a Multimedia Perspective: Part I Isaac Sim Simulation Platform

The emergence of physical AI represents a fundamental shift in intelligent systems, yet its distinction from embodied AI and its reliance on high-fidelity simulation remain under-explored by the multimedia community. This tutorial clarifies the identity of physical AI through its core computational triad and delineates its relationship with embodied AI, proposing a synergistic architecture for their future convergence. We position NVIDIA Isaac Sim as the pivotal platform for this ecosystem, detailing its pipeline for creating high-fidelity intelligent digital twins and facilitating multi-sensor simulation as a cornerstone of multimedia computing. Using a standalone workflow, we construct a multi-sensor environment where physical sensor readings are strictly synchronized with visual rendering. Finally, we envision the evolution of the underlying connectivity layer into an Agentic Network. We discuss how future infrastructure, enabled by agentic protocols, will support the dynamic interaction between these high-fidelity simulations and physical realities, paving the way for the next generation of autonomous systems.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Multimedia Computing Communications and Applications
Published
2026-09-15
DOI
https://doi.org/10.1145/3847667
Primary Topic
Human Motion and Animation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

A Tutorial on Physical AI from a Multimedia Perspective: Part I Isaac Sim Simulation Platform

Haiwei Dong, Jianquan Wang, Abdulmotaleb El Saddik, Lin Yang
ACM Transactions on Multimedia Computing Communications and Applications
Human Motion and Animation
article

A Tutorial on Physical AI from a Multimedia Perspective: Part I Isaac Sim Simulation Platform

Haiwei Dong, Jianquan Wang, Abdulmotaleb El Saddik, Lin Yang
article en

Abstract

The emergence of physical AI represents a fundamental shift in intelligent systems, yet its distinction from embodied AI and its reliance on high-fidelity simulation remain under-explored by the multimedia community. This tutorial clarifies the identity of physical AI through its core computational triad and delineates its relationship with embodied AI, proposing a synergistic architecture for their future convergence. We position NVIDIA Isaac Sim as the pivotal platform for this ecosystem, detailing its pipeline for creating high-fidelity intelligent digital twins and facilitating multi-sensor simulation as a cornerstone of multimedia computing. Using a standalone workflow, we construct a multi-sensor environment where physical sensor readings are strictly synchronized with visual rendering. Finally, we envision the evolution of the underlying connectivity layer into an Agentic Network. We discuss how future infrastructure, enabled by agentic protocols, will support the dynamic interaction between these high-fidelity simulations and physical realities, paving the way for the next generation of autonomous systems.

ACM Transactions on Multimedia Computing Communications and Applications
University of Ottawa (CA), Infineon Technologies (Canada) (CA), DayStar (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 15%
Human Motion and Animation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Tutorial on Physical AI from a Multimedia Perspective: Part I Isaac Sim Simulation Platform — Haiwei Dong, Jianquan Wang, et al. · ACM Transactions on Multimedia Computing Communications and Applications (2026) | TGRS Research Map | TGRS