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全部/科技/Robotics Dev Blog

NVIDIA · Robotics Dev Blog

3分钟前更新

01
Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T
As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids...Elizabeth Goodman
02
Inside NVIDIA Halos for Robotics: A Full-Stack Functional Safety System for Physical AI
Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional...Suhas Hariharapura Sheshadri
03
Pretrained to Imagine, Fine-Tuned to Act: The Rise of World-Action Models
Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it...Moritz Reuss
04
Deploy Agentic-Ready AI at the Edge with Memory Efficiency in NVIDIA JetPack 7.2
As AI agents move from the digital world to the physical environment, they can readily use NVIDIA Jetson to accelerate real-world deployment with optimized...Peilun Tsai
05
How to Post-Train Autonomous Vehicle Models in Closed-Loop with NVIDIA Alpamayo
Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can...Boris Ivanovic
06
Develop Physical AI Reasoning, World, and Action Models with NVIDIA Cosmos 3
Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what's...Asawaree Bhide
07
Maximizing Memory Efficiency to Run Bigger Models on NVIDIA Jetson
The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these...Anshuman Bhat
08
Integrate Physical AI Capabilities into Existing Apps with NVIDIA Omniverse Libraries
Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and...Ashley Goldstein
09
Build and Stream Browser-Based XR Experiences with NVIDIA CloudXR.js
Delivering high-fidelity VR and AR experiences to enterprise users has typically required native application development, custom device management, and complex...Yanzi Zhu
10
How Centralized Radar Processing on NVIDIA DRIVE Enables Safer, Smarter Level 4 Autonomy
In the current state of automotive radar, machine learning engineers can't work with camera-equivalent raw RGB images. Instead, they work with the output of...Lachlan Dowling
11
NVIDIA IGX Thor Powers Industrial, Medical, and Robotics Edge AI Applications
Industrial and medical systems are rapidly increasing the use of high-performance AI to improve worker productivity, human-machine interaction, and downtime...Suhas Hariharapura Sheshadri
12
Using Simulation to Build Robotic Systems for Hospital Automation
Healthcare faces a structural demand–capacity crisis: a projected global shortfall of ~10 million clinicians by 2030, billions of diagnostic exams annually...Mingxin Zheng
13
Newton Adds Contact-Rich Manipulation and Locomotion Capabilities for Industrial Robotics
Physics forms the foundation of robotic simulation, enabling realistic modeling of motion and interaction. For tasks like locomotion and manipulation,...Philipp Reist
14
Scale Synthetic Data and Physical AI Reasoning with NVIDIA Cosmos World Foundation Models
The next generation of AI-driven robots like humanoids and autonomous vehicles depends on high-fidelity, physics-aware training data. Without diverse and...Pranjali Joshi
15
Build Accelerated, Differentiable Computational Physics Code for AI with NVIDIA Warp
Computer-aided engineering (CAE) is shifting from human-driven workflows toward AI-driven ones, including physics foundation models that generalize across...Sheel Nidhan
16
Build Next-Gen Physical AI with Edge‑First LLMs for Autonomous Vehicles and Robotics
Physical AI is rapidly evolving, from next-generation software-defined autonomous vehicles (AVs) to humanoid robots. The challenge is no longer how to run a...Lin Chai
17
R²D²: Scaling Multimodal Robot Learning with NVIDIA Isaac Lab
Building robust, intelligent robots requires testing them in complex environments. However, gathering data in the physical world is expensive, slow, and often...Oyindamola Omotuyi
18
Building Generalist Humanoid Capabilities with NVIDIA Isaac GR00T N1.6 Using a Sim-to-Real Workflow
To make humanoid robots useful, they need cognition and loco-manipulation that span perception, planning, and whole-body control in dynamic environments. ...Edith Llontop
19
Accelerating LLM and VLM Inference for Automotive and Robotics with NVIDIA TensorRT Edge-LLM
Large language models (LLMs) and multimodal reasoning systems are rapidly expanding beyond the data center. Automotive and robotics developers increasingly want...Lin Chai
20
Build and Orchestrate End-to-End SDG Workflows with NVIDIA Isaac Sim and NVIDIA OSMO
As robots take on increasingly dynamic mobility tasks, developers need physics-accurate simulations that translate across environments and workloads. Training...Asawaree Bhide
21
Simplify Generalist Robot Policy Evaluation in Simulation with NVIDIA Isaac Lab-Arena
Generalist robot policies must operate across diverse tasks, embodiments, and environments, requiring scalable, repeatable simulation-based evaluation. Setting...Sangeeta Subramanian
22
Accelerate AI Inference for Edge and Robotics with NVIDIA Jetson T4000 and NVIDIA JetPack 7.1
NVIDIA is introducing the NVIDIA Jetson T4000, bringing high-performance AI and real-time reasoning to a wider range of robotics and edge AI applications....Shashank Maheshwari
23
Building Autonomous Vehicles That Reason with NVIDIA Alpamayo
Autonomous vehicle (AV) research is undergoing a rapid shift. The field is being reshaped by the emergence of reasoning-based vision–language–action (VLA)...Marco Pavone
24
AI Factories, Physical AI, and Advances in Models, Agents, and Infrastructure That Shaped 2025
2025 was another milestone year for developers and researchers working with NVIDIA technologies. Progress in data center power and compute design, AI...Michelle Horton
25
Simulate Robotic Environments Faster with NVIDIA Isaac Sim and World Labs Marble
Building realistic 3D environments for robotics simulation has traditionally been a labor-intensive process, often requiring weeks of manual modeling and setup....Wonsik Han
26
R²D²: Improving Robot Manipulation with Simulation and Language Models
Robot manipulation systems struggle with changing objects, lighting, and contact dynamics when they move into dynamic real-world environments. On top of this,...Asawaree Bhide
27
Getting Started with Edge AI on NVIDIA Jetson: LLMs, VLMs, and Foundation Models for Robotics
Running advanced AI and computer vision workloads on small, power-efficient devices at the edge is a growing challenge. Robots, smart cameras, and autonomous...Chitoku Yato
28
How to Scale Data Generation for Physical AI with the NVIDIA Cosmos Cookbook
Building powerful physical AI models requires diverse, controllable, and physically-grounded data at scale. Collecting large-scale, diverse real-world datasets...Prachi Mishra
29
Making Robot Perception More Efficient on NVIDIA Jetson Thor
Building autonomous robots requires robust, low-latency visual perception for depth, obstacle recognition, localization, and navigation in dynamic environments....Chintan Intwala
30
R²D²: Perception-Guided Task & Motion Planning for Long-Horizon Manipulation
Traditional task and motion planning (TAMP) systems for robot manipulation use cases operate on static models that often fail in new environments. Integrating...Raffaello Bonghi