# Physical AI Published articles for Physical AI. 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. ## Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video DevFeed: [Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video]() Original publisher: [Read original article]() Author: Sasa Docca Published: 2026-09-10T16:30:35Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [foundation-models](), [Cosmos](), [Simulation and Design](), [synthetic-data]() Tags: [ai](), [artificial-intelligence](), [cosmos](), [customer-stories](), [industrial-and-manufacturing](), [isaac](), [model](), [nvidia](), [nvidia-blackwell](), [omniverse](), [physical-ai](), [robotics](), [robots](), [simulation-and-design](), [synthetic-data-generation](), [tensorrt](), [video]() ### AI overview Skild AI's S1 robot foundation model learns new long-horizon physical tasks from a single video demonstration through in-context learning, without task-specific retraining. The article describes its development on NVIDIA AI infrastructure and use of NVIDIA Isaac Lab and Cosmos technologies. ### Source excerpt Manufacturing floors, warehouses and production lines rarely stay fixed -- tasks change, layouts shift and new products arrive, and most robots can't keep up without significant reprogramming. Skild AI's new S1 robot foundation model helps address this, designed to learn previously unseen, long-horizon tasks from a single video demonstration. The model, launched last week, uses [...] ## Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies DevFeed: [Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies]() Original publisher: [Read original article]() Author: Ali Kani Published: 2026-09-10T16:00:04Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Training AI Models](), [SDKs]() Tags: [ai-models](), [ai-training](), [artificial-intelligence](), [autonomous-vehicles](), [cosmos](), [customer-stories](), [datasets](), [driving](), [mobility](), [nvidia](), [nvidia-blackwell](), [nvidia-dgx](), [nvidia-drive](), [nvidia-halos](), [omniverse](), [physical-ai](), [simulation-and-design]() ### AI overview NVIDIA describes an open robotaxi platform for training AI driving models, simulation and safety validation, and real-time in-vehicle computing. ### Source excerpt The global robotaxi market -- physical AI's first commercial breakthrough -- is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world's busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is [...] ## Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson DevFeed: [Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-04T16:21:04Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson](), [model-deployment](), [Inference](), [Inference Performance](), [Optimization](), [Mixture of Experts (MoE)]() Tags: [agentic-ai-generative-ai](), [edge](), [edge-computing](), [how-to](), [inference](), [inference-performance](), [jetpack](), [jetson](), [jetson-orin](), [mixture-of-experts-moe](), [nvfp4](), [optimization](), [physical-ai](), [reasoning](), [thor](), [tutorial](), [vllm]() ### AI overview A tutorial on deploying and optimizing compact reasoning and agentic AI models on NVIDIA Jetson. It covers choosing models, improving inference with NVFP4 quantization and speculative decoding, serving example models with vLLM, and validating a configuration for a workload. ### Source excerpt Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run... ## 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. ## Arduino Core on Zephyr 1.0.0 is here! DevFeed: [Arduino Core on Zephyr 1.0.0 is here!]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-03T15:20:44Z Content type: release Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Release notes](), [Hardware](), [real-time](), [Robotics](), [Automation](), [Qualcomm]() Tags: [arduino](), [arduino-core-on-zephyr](), [arduino-ide](), [bug-fixes](), [camera](), [image-classification](), [object-detection](), [physical-ai](), [qualcomm](), [real-time](), [release](), [robotics](), [ventuno-q](), [zephyr]() ### AI overview Arduino announced ArduinoCore-Zephyr 1.0.0, adding support for the VENTUNO Q board, updating the base to Zephyr 4.4.1, enabling camera support for the Nicla Vision module, and including bug fixes and cleanup. ### Source excerpt We're excited to announce version 1.0.0 of the ArduinoCore-Zephyr, our biggest release yet. Building on the stability milestone we hit with 0.90.0, this release brings new hardware support, an updated Zephyr base, and a long list of fixes and improvements across the core. Thank you to everyone who tested 0.90.0, filed issues, and helped us [...] The post Arduino Core on Zephyr 1.0.0 is here! appeared first on Arduino Blog. ## Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec DevFeed: [Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nvidia](), [synthetic-data](), [Simulation and Design](), [data](), [configuration]() Tags: [3d](), [autonomous](), [autonomous-vehicles](), [data](), [developer-tools-techniques](), [developers](), [development](), [model](), [models](), [nvidia](), [omniverse](), [performance](), [physical-ai](), [post](), [robotics](), [scale](), [simulation-modeling-design](), [synthetic-data](), [training](), [tutorial]() ### AI overview This tutorial explains how to adapt an autonomous-vehicle perception stack across carline and sensor-rig variants using existing real-world drives. It presents a four-step workflow with NVIDIA Omniverse NuRec: pair a reconstructed drive with a target rig, render target camera views, refine the frames with NVIDIA Harmonizer, and train a perception model on the output. ### Source excerpt A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline--for example, from an SUV to a sedan or another vehicle... ## How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents DevFeed: [How to Train a Cross-Embodiment Robot Navigation Policy with AI Agents]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-26T20:05:06Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Reinforcement learning](), [codex](), [coding](), [Simulation and Design](), [CUDA]() Tags: [agent](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-agents](), [architecture](), [codex](), [coding](), [cuda](), [development](), [evaluation](), [framework](), [nvidia](), [omniverse](), [physical-ai](), [reinforcement-learning](), [robotics](), [simulation-modeling-design](), [skills](), [testing](), [training](), [tutorial](), [workflow]() ### AI overview This tutorial presents an agent-driven COMPASS workflow for training and evaluating cross-embodiment robot navigation policies. It covers asset preparation, smoke testing, residual reinforcement learning, checkpoint evaluation, runtime integration, and optional reconstructed environments using NVIDIA Omniverse NuRec. ### Source excerpt Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to... ## 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. ## Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control DevFeed: [Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Cosmos](), [AI, ML & Data Engineering]() Tags: [agentic-ai-generative-ai](), [cosmos](), [deployment](), [edge](), [edge-computing](), [featured](), [foundation-models](), [inference](), [jetson](), [latency](), [nvidia](), [on-device](), [physical-ai](), [post-training](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [thor](), [tutorial]() ### AI overview A tutorial on post-training NVIDIA Cosmos 3 Edge as an on-device robot manipulation policy, serving it on Jetson Thor, running receding-horizon inference, and evaluating it in closed-loop simulation. ### Source excerpt Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for... ## Beyond VLAs: How World Action Models Reshape Robot Manipulation DevFeed: [Beyond VLAs: How World Action Models Reshape Robot Manipulation]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-04T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Robotics](), [World models](), [vlm](), [post-training](), [Cosmos](), [Nvidia](), [NVIDIA Research]() Tags: [agentic-ai-generative-ai](), [cosmos](), [featured](), [nvidia](), [nvidia-research](), [physical-ai](), [post-training](), [robot-manipulation](), [robotics](), [simulation-modeling-design](), [thor](), [vlm](), [world-model](), [zero-shot]() ### AI overview The article explains how World Action Models (WAMs) use video world models as backbones for robot policies, addressing the physical-generalization limitations of vision-language-action models. It discusses post-training WAMs into specialized policies and presents NVIDIA Cosmos 3 as a foundation for building them. ### Source excerpt A central challenge in robotics is building policies that generalize beyond the demonstrations they're trained on. A policy that succeeds in a training scene... ## Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration DevFeed: [Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration]() Original publisher: [Read original article]() Author: Steven Hansen Published: 2026-07-30T15:00:59Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [AI Bots]() Tags: [api](), [collaboration](), [gemini](), [multimodal](), [none](), [orchestration](), [physical-ai](), [robotics](), [tools](), [video]() ### AI overview Gemini Robotics ER 2 is a robotics embodied-reasoning model that uses continuous video, orchestrates tools and lower-level controls, and supports multi-robot collaboration. It is available to developers through the Gemini API and Google AI Studio, with private preview on Gemini Enterprise Agent Platform. ### Source excerpt Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications. ## Developing Healthcare Robotics with GPU-Native Medical Physics Simulation DevFeed: [Developing Healthcare Robotics with GPU-Native Medical Physics Simulation]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-28T20:49:21Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Isaac for Healthcare](), [Robotics](), [Reinforcement learning](), [GPU](), [datasets](), [Medical imaging](), [Cosmos](), [Open Source](), [real-time](), [Nvidia]() Tags: [agentic-ai-generative-ai](), [data](), [datasets](), [featured](), [gpu](), [healthcare](), [isaac](), [isaac-for-healthcare](), [isaac-sim](), [medical-imaging](), [nvidia](), [open-source](), [physical-ai](), [physics](), [real-time](), [reinforcement-learning](), [robotics](), [simulation-modeling-design](), [warp](), [world-model]() ### AI overview The article presents NVIDIA's open source, GPU-accelerated Medical Physics Simulation framework for healthcare robotics. It addresses limited medical robotics data, poor generalization, and slow development by enabling anatomical digital twins, device-anatomy and medical imaging simulation, and GPU-scale reinforcement learning within Isaac for Healthcare, Isaac Sim, and Isaac Lab. ### Source excerpt Unlike autonomous driving or industrial robotics, healthcare robotics can't rely on internet-scale data collection or unlimited real-world experimentation.... ## 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. ## Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps DevFeed: [Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-20T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [SDKs]() Tags: [3d](), [agent-skill](), [apis](), [apps](), [autonomous-vehicles](), [c](), [featured](), [lidar](), [nvidia](), [omniverse](), [openusd](), [physical-ai](), [python](), [robotics](), [simulation-modeling-design]() ### AI overview The article explains how to integrate NVIDIA Omniverse's ovrtx RTX sensor-simulation library into existing applications. It covers using its C and Python SDK to generate camera, lidar, radar, and related sensor outputs from OpenUSD scenes. ### Source excerpt Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and... ## Develop Lightweight USD Runtimes Faster with AI Agents DevFeed: [Develop Lightweight USD Runtimes Faster with AI Agents]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-15T21:57:23Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation and Design](), [Code](), [Nvidia]() Tags: [agents](), [ai](), [ai-agents](), [code](), [developer-tools-techniques](), [featured](), [hackathon](), [nvidia](), [omniverse](), [openusd](), [physical-ai](), [robotics](), [simulation-modeling-design]() ### AI overview This article presents nanousd-labs, an experimental NVIDIA Omniverse Labs project that uses AI agents and the machine-readable USD Core Specification to generate lightweight, compliant OpenUSD runtimes. It explains how agents generate and validate code against specification-derived tests, enabling runtimes tailored to memory, performance, language, and deployment constraints. ### Source excerpt OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation... ## Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills DevFeed: [Post-Train NVIDIA Cosmos 3 in One Day Using Agent Skills]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-14T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Cosmos](), [Agent Skills](), [post-training](), [lora](), [Nvidia]() Tags: [agent-skills](), [agentic-ai-generative-ai](), [ai-agent](), [computer-vision-video-analytics](), [cosmos](), [featured](), [fine-tuning](), [lora](), [physical-ai](), [post-training](), [simulation-modeling-design](), [training-ai-models]() ### AI overview A tutorial on post-training NVIDIA Cosmos 3 Nano for video question answering with coding-agent skills, LoRA, and TAO AutoML configuration sweeps. ### Source excerpt What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning... ## 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... ## Powering the future of robotics in Europe DevFeed: [Powering the future of robotics in Europe]() Original publisher: [Read original article]() Author: Carolina Parada Published: 2026-06-09T14:02:33Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [AI, ML & Data Engineering]() Tags: [ai](), [europe](), [google](), [none](), [physical-ai](), [robotics](), [startup]() ### AI overview Google DeepMind announces a three-month accelerator for early-stage European robotics startups, offering AI models, technical expertise, mentorship, and product guidance. ### Source excerpt Google DeepMind Accelerator selects 15 robotics companies from across Europe to join the program. Providing 3 months of intensive mentorship and technical support, enabl... ## Real-world grounding in agentic AI DevFeed: [Real-world grounding in agentic AI]() Original publisher: [Read original article]() Author: Rose Yu Published: 2026-06-08T19:00:00Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [AI Bots](), [dashboards]() Tags: [agentic-ai](), [agentic-ai-grounding](), [ai](), [ai-agent-hallucinations](), [ai-agents](), [calibrated-uncertainty](), [formal-verification-ai](), [foundation-models-physical-ai](), [hallucinations](), [large-language-models-llms](), [machine-learning](), [physical-ai](), [physics-guided-deep-learning](), [robotics](), [robots](), [uncertainty-quantification-llms]() ### AI overview The article proposes four approaches for grounding AI agents in physical-world operational environments. It argues that integrating domain data, physical principles, and simulations can reduce harmful hallucinations and improve safe, trustworthy agent behavior. ### Source excerpt Four approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments. ## Introducing Strands Labs: Get hands-on today with state-of-the-art, experimental approaches to agentic development DevFeed: [Introducing Strands Labs: Get hands-on today with state-of-the-art, experimental approaches to agentic development]() Original publisher: [Read original article]() Author: Joy Chakraborty Published: 2026-02-23T18:24:30Z Content type: release Language: en Sources: [AWS Open Source Blog]() Topics: [AI, ML & Data Engineering](), [SDKs]() Tags: [agentic-ai](), [agents](), [ai-development](), [github](), [open-source](), [physical-ai](), [python](), [robots](), [strands-agents](), [typescript]() ### AI overview AWS introduces Strands Labs, a separate GitHub organization for experimental agentic AI projects built around the open-source Strands Agents SDK. Its launch projects include Robots, Robots Sim, and AI Functions. ### Source excerpt We're introducing Strands Labs, a new Strands GitHub organization designed to give developers the ability to get hands-on with experimental, state-of-the-art approaches to agentic AI development. The Strands Agents SDK - available for both Python and TypeScript - has gained incredible traction in the developer community since we released it as open source in May [...] ## 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. [Next page]()