# Physical AI Physical AI is a field involving hardware and software systems that perceive, reason about, learn from, and act in the physical world using sensors and actuators. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in... ## Building smarter AMRs with the Arduino® VENTUNO™ Q board DevFeed: [Building smarter AMRs with the Arduino® VENTUNO™ Q board]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-11T11:10:16Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Physical AI](), [Microcontroller](), [Linux](), [Computer vision](), [real-time](), [systems]() Tags: [amr](), [arduino](), [autonomous-mobile-robots](), [computer-vision](), [featured](), [linux](), [mcu](), [physical-ai](), [real-time](), [robotics](), [robots](), [ros](), [sensor](), [sensors](), [ventuno-q]() ### AI overview The article explains how Arduino's VENTUNO Q board could support autonomous mobile robots by combining a Linux-capable MPU with a real-time MCU. The MPU can run Linux, ROS 2, navigation, computer vision, and AI workloads, while the MCU handles motor control, encoder feedback, inertial measurements, local sensing, and motor-driver communication. ### Source excerpt Physical AI is based on the idea that intelligence shouldn't stop at perception: instead, it should bring to life systems able to sense their environment, reason about it, and act on it - all in one continuous loop. It's what makes the difference between a device that observes and one that acts. Autonomous mobile robots [...] The post Building smarter AMRs with the Arduino® VENTUNO™ Q board appeared first on Arduino Blog. ## Petoi Quaddle - A mini robot dog for physical AI experimentation (Crowdfunding) DevFeed: [Petoi Quaddle - A mini robot dog for physical AI experimentation (Crowdfunding)]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-04T08:03:49Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Robotics](), [Physical AI](), [ESP32-S3](), [Arduino](), [Raspberry Pi](), [coding](), [Open Source]() Tags: [3d-printing](), [arduino](), [broadcom-bcmxxxx](), [c-c-plus-plus](), [crowdfunding](), [edge-ai](), [education](), [esp32](), [esp32-s3](), [esphome](), [espressif](), [hardware](), [micropython](), [open-source](), [physical-ai](), [raspberry-pi](), [robot-dog](), [robotics](), [ros](), [simulator](), [smart-home](), [smart-speaker](), [stem](), [video]() ### AI overview The Petoi Quaddle is a mini robot dog designed for learning robotics, coding, physical AI, and 3D printing. It uses an ESP32-S3 motion core, optional AI hardware, OpenCat firmware, and multiple programming environments. Petoi launched it on Kickstarter with three models and planned December 2026 shipping. ### Source excerpt If you are not into ducks, but still would like to play around with physical AI, the Petoi Quaddle is a mini robot dog equipped with 4 servos controlling its legs and is designed to help people learn robotics, coding, AI, and 3D printing. The robot is powered by an ESP32-S3 motion core for servo control and wireless connectivity, and an optional ESP32-S3 AI core to enable voice commands and Smart Home control. The robot can walk, strafe, spin, talk back, and react to the user's touch. Three models are available depending on the user's requirements: Builder, Buddy, and Scout. A Raspberry Pi Zero can also be mounted on the top in place of the ESP32-S3-based AI core. Petoi Quaddle runs OpenCat open-source firmware (Arduino) already used in the earlier Petoi Bittle robot dog, first introduced in 2020. The robot can be programmed in a visual programming IDE with drag-and-drop [...] The post Petoi Quaddle - A mini robot dog for physical AI experimentation (Crowdfunding) appeared first on CNX Software - Embedded Systems News. ## From voice command to robotic arm: how agentic AI on the edge is changing the factory floor DevFeed: [From voice command to robotic arm: how agentic AI on the edge is changing the factory floor]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-01T12:20:24Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Artificial Intelligence](), [foundation-models](), [Automation](), [Arduino](), [UNO Q](), [AI Agent](), [Physical AI](), [multimodal]() Tags: [agentic-ai](), [ai](), [arduino](), [automation](), [featured](), [foundation-models](), [industrial](), [inference](), [latency](), [manufacturing](), [multimodal](), [robotic-arm](), [robotics](), [smart-factory](), [uno-q](), [usb](), [voice-commands](), [voice-control]() ### AI overview The article describes a demonstration in which Forgis uses a foundation model running on an Arduino UNO Q board to convert voice commands into robotic-arm actions. The system processes multimodal factory data and performs inference locally, enabling real-time control without a cloud round trip. ### Source excerpt For years, bringing real intelligence to industrial automation meant expensive infrastructure, proprietary systems, and steep learning curves. That's changing - fast. Foundation models powerful enough to run at the edge are turning natural language into machine control, and the factory floor is starting to look a lot more like a conversation. AI as the new [...] The post From voice command to robotic arm: how agentic AI on the edge is changing the factory floor appeared first on Arduino Blog. ## Sergey Levine: Current State of Humanoid Robotics, China & Future Predictions DevFeed: [Sergey Levine: Current State of Humanoid Robotics, China & Future Predictions]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-08-24T13:05:33Z Content type: opinion Language: en Sources: [The Developing Dev]() Topics: [Robotics](), [Humanoid Robots](), [Physical AI](), [Machine learning](), [Large Language Model]() Tags: [china](), [future](), [humanoid-robots](), [llms](), [machine-learning](), [physical-ai](), [predictions](), [robotics]() ### AI overview An interview with robotics researcher Sergey Levine examines the current state of humanoid robotics, China's robotics ecosystem, possible competitors, future timelines, and how humanoid robots may be deployed. Levine discusses the importance of scaling the right technology, data, and model size. ### Source excerpt Sergey Levine is one of the world's top robotics researchers and co-founder of Physical Intelligence. ## RAISE Summit 2026: What I Learned About AI, Robotics, Agents, and Infrastructure DevFeed: [RAISE Summit 2026: What I Learned About AI, Robotics, Agents, and Infrastructure]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2026-07-25T07:00:53Z Content type: opinion Language: en Sources: [Neural Bits]() Topics: [Artificial Intelligence](), [Physical AI](), [Robotics](), [World models](), [Computer vision]() Tags: [ai](), [conferences](), [models](), [physical-ai](), [robotics](), [vision]() ### AI overview A personal account of RAISE and MACHINA Summit 2026 in Paris, covering conference themes and observations about AI, robotics, embodied AI, world models, perception, and the differences between language-oriented models and models used for robotic perception and action. ### Source excerpt Key takeaways from talks, demos, and conversations at RAISE and MACHINA conferences in Paris this year. ## MIT in the media: Innovating and educating for the next 250 years of America DevFeed: [MIT in the media: Innovating and educating for the next 250 years of America]() Original publisher: [Read original article]() Published: 2026-07-01T20:30:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Physical AI](), [Learning](), [Machine learning]() Tags: [ai](), [artificial-intelligence](), [curiosity](), [education-teaching-academics](), [funding](), [health](), [learning](), [physical-ai](), [president-sally-kornbluth](), [research](), [students](), [teaching](), [teamwork](), [university]() ### AI overview At a Washington Post Live panel, MIT President Sally Kornbluth discussed curiosity-driven research, university preparation for an AI-enabled future, and a human-centric approach to AI education. She emphasized foundational STEM knowledge, ethical and civic education, physical AI, collaboration, and AI as an augmentation tool. ### Source excerpt During a "Washington Post Live" panel discussion with ASU President Michael Crow, President Sally Kornbluth explored how universities are preparing the next generation of scientists to lead in America's rapidly changing technological landscape. ## Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools DevFeed: [Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-06-30T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Omniverse](), [Optimization](), [Tooling](), [GPU](), [Nvidia](), [CUDA](), [PyTorch](), [Physical AI](), [Robotics](), [Synthetic Data Generation](), [real-time](), [Reinforcement learning]() Tags: [3d](), [ai](), [autonomous](), [autonomous-vehicles](), [cuda](), [developer-tools](), [developer-tools-techniques](), [driving](), [engineering](), [featured](), [gpu](), [lidar](), [machine-learning](), [nvidia](), [omniverse](), [optimization](), [performance](), [physical-ai](), [real-time](), [reinforcement-learning](), [simulation-modeling-design](), [synthetic-data-generation]() ### AI overview This article explains how NVIDIA Nsight Developer Tools can optimize the NVIDIA Omniverse NuRec neural reconstruction pipeline. It focuses on reducing GPU-intensive reconstruction and rendering costs to improve engineering iteration and move toward real-time performance. ### Source excerpt NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such... ## NVIDIA Cosmos Reason 2 Brings Advanced Reasoning To Physical AI DevFeed: [NVIDIA Cosmos Reason 2 Brings Advanced Reasoning To Physical AI]() Original publisher: [Read original article]() Author: Tsung-Yi Lin; Debraj Sinha Published: 2026-01-05T22:56:51Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [Cosmos](), [Physical AI](), [vlm](), [Artificial Intelligence](), [Video Analytics](), [long-context](), [Deployment](), [data](), [datasets](), [Cloud]() Tags: [agents](), [ai](), [ai-agents](), [analytics](), [cloud](), [cosmos](), [data](), [datasets](), [deployment](), [language-models](), [long-context](), [models](), [nvidia](), [ocr](), [open](), [performance](), [physical-ai](), [physics](), [reasoning](), [vision]() ### AI overview NVIDIA released Cosmos Reason 2, an open reasoning vision-language model for physical AI. The model is designed to help robots and AI agents understand, plan, and act in the physical world, with improved spatio-temporal reasoning, visual perception, OCR, long-context input, and deployment from edge to cloud. ### Source excerpt NVIDIA today released Cosmos Reason 2, the latest advancement in open, reasoning vision language models for physical AI. Cosmos Reason 2 surpasses its previous version in accuracy and tops the Physical AI Bench and Physical Reasoning leaderboards as the #1 open model for visual understanding. Since their introduction, vision-language models have rapidly improved at tasks like object and pattern recognition in images. ## NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets DevFeed: [NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets]() Original publisher: [Read original article]() Author: Ming-Yu Liu; Hanzi Mao; Jinwei Gu; Pranjali Joshi; Asawaree Published: 2025-03-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Cosmos](), [Physical AI](), [Artificial Intelligence](), [Robotics](), [Synthetic Data Generation](), [datasets](), [autonomous vehicles](), [foundation-models](), [Isaac](), [Omniverse](), [Open Source]() Tags: [ai](), [artificial-intelligence](), [autonomous-vehicles](), [community](), [cosmos](), [datasets](), [foundation-models](), [isaac](), [nvidia](), [omniverse](), [open-source](), [physical-ai](), [research](), [robotics](), [synthetic-data-generation]() ### AI overview NVIDIA announces Cosmos Transfer, an open world foundation model that generates controllable, photorealistic video scenes from structured inputs such as depth maps, trajectories, LiDAR scans, and 3D bounding boxes. Together with NVIDIA Omniverse, it supports synthetic data generation for robotics and autonomous vehicle development. NVIDIA also introduces an open Physical AI Dataset on Hugging Face and the Isaac GR00T N1 foundation model for humanoid robot reasoning. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.