Field AI is building robot brains that enable robots to autonomously manage a wide range of industrial processes. Vention creates pretrained skills to ease development of robotic tasks. And Cobot offers Proxie, an AI-powered cobot designed to handle material movement and adapt to dynamic environments, working seamlessly alongside humans.
These leading robotics startups are all making advances using
NVIDIA Isaac Sim
on Amazon Web Services. Isaac Sim is a reference application built on
NVIDIA Omniverse
for developers to simulate and test AI-driven robots in physically based virtual environments.
NVIDIA announced at AWS re:Invent today that Isaac Sim now runs on Amazon Elastic Cloud Computing (EC2) G6e instances accelerated by
NVIDIA L40S GPUs
. And with
NVIDIA OSMO
, a cloud-native orchestration platform, developers can easily manage their complex robotics workflows across their AWS computing infrastructure.
This combination of NVIDIA-accelerated hardware and software — available on the cloud — allows teams of any size to scale their physical AI workflows.
Physical AI
describes AI models that can understand and interact with the physical world. It embodies the next wave of
autonomous machines and robots
, such as self-driving cars, industrial manipulators, mobile robots, humanoids and even robot-run infrastructure like factories and warehouses.
With physical AI, developers are embracing a
three computer solution
for training, simulation and inference to make breakthroughs.
Yet physical AI for robotics systems requires robust training datasets to achieve precision inference in deployment. Developing such datasets, however, and testing them in real situations can be impractical and costly.
Simulation offers an answer, as it can significantly accelerate the training, testing and deployment of AI-driven robots.
Harnessing L40S GPUs in the Cloud to Scale Robotics Simulation and Training
Simulation is used to verify, validate and optimize robot designs as well as the systems and their algorithms before deployment. Simulation can also optimize facility and system designs before construction or remodeling starts for maximum efficiencies, reducing costly manufacturing change orders.
Amazon EC2 G6e instances accelerated by NVIDIA L40S GPUs provide a 2x performance gain over the prior architecture, while allowing the flexibility to scale as scene and simulation complexity grows. The instances are used to train many computer vision models that power AI-driven robots. This means the same instances can be extended for various tasks, from data generation to simulation to model training.
Using
NVIDIA OSMO
in the cloud allows teams to orchestrate and scale complex ‌robotics development workflows across distributed computing resources, whether on premises or in the AWS cloud.
Isaac Sim provides access to the latest
robotics simulation capabilities
and the cloud, fostering collaboration. One of the critical workflows is generating synthetic data for perception model training.
Using a
reference workflow
that combines
NVIDIA Omniverse Replicator
, a framework for building custom synthetic data generation (SDG) pipelines and a core extension of Isaac Sim, with
NVIDIA NIM microservices
, developers can build generative AI-enabled SDG pipelines.
These include the USD Code NIM microservice for generating Python USD code and answering OpenUSD queries, and the USD Search NIM microservice for exploring OpenUSD assets using natural language or image inputs. The Edify 360 HDRi NIM microservice generates 360-degree environment maps, while the Edify 3D NIM microservice creates ready-to-edit 3D assets from text or image prompts. This eases the synthetic data generation process by reducing many tedious and manual steps, from asset creation to image augmentation, using the power of generative AI.
Rendered.ai’s
synthetic data engineering platform integrated with Omniverse Replicator enables companies to generate synthetic data for computer vision models used in industries from security and intelligence to manufacturing and agriculture.
SoftServe
, an IT consulting and digital services provider, uses Isaac Sim to generate synthetic data and validate robots used in vertical farming with Pfeifer & Langen, a leading European food producer.
Tata Consultancy Services
is building custom synthetic data generation pipelines to power its Mobility AI suite to address automotive and autonomous use cases by simulating real-world scenarios. Its applications include defect detection, end-of-line quality inspection and hazard avoidance.
Learning to Be Robots in Simulation
While Isaac Sim enables developers to test and validate robots in physically accurate simulation,
Isaac Lab
, an open-source robot learning framework built on Isaac Sim, provides a virtual playground for building robot policies that can run on
AWS Batch
.
Because these simulations are repeatable, developers can easily troubleshoot and reduce the number of cycles required for validation and testing.
Several robotics developers are embracing
NVIDIA Isaac
on AWS to develop physical AI, such as:
Aescape’s robots
are able to provide precision-tailored massages by accurately modeling and tuning onboard sensors in Isaac Sim.
Cobot
has used Isaac Sim with its AI-powered cobot, Proxie, to optimize logistics in warehouses, hospitals, manufacturing sites, and more.
Cohesive Robotics
has integrated Isaac Sim into its software framework called Argus OS for developing and deploying robotic workcells used in high-mix manufacturing environments.
Field AI, a builder of robot foundation models, uses Isaac Sim and Isaac Lab to evaluate the performance of its models in complex, unstructured environments across industries such as construction, manufacturing, oil and gas, mining and more.
Standard Bots
is simulating and validating the performance of its R01 robot used in manufacturing and machining setup.
Swiss Mile
is using Isaac Sim and Isaac Lab for robot learning so that wheeled quadruped robots can perform tasks autonomously with new levels of efficiency in factories and warehouses.
Vention
, which offers a full-stack cloud-based automation platform, is harnessing Isaac Sim for developing and testing new capabilities for robot cells used by small to medium-size manufacturers.
Learn more about Isaac Sim 4.2, now available on Amazon EC2 G6e instances powered by NVIDIA L40S GPUs on
AWS Marketplace
.
Categories:
Robotics
Tags:
NVIDIA Isaac Sim
|
Omniverse Enterprise
|
Physical AI
Field AI 正在建構機器人大腦，讓機器人得以自主管理各項工業流程。Vention 創造預先訓練好的技能，以簡化機器人任務的開發。而 Cobot 則提供一個由人工智慧（AI）驅動的協作機器人 Proxie，可處理材料移動並適應動態環境，與人類一起無縫合作。
這些領先的機器人新創公司皆使用 Amazon Web Services（AWS）上的
NVIDIA Isaac Sim
來取得進展。Isaac Sim 是建置在
NVIDIA Omniverse
上的參考應用程式，供開發人員在以物理原則為基礎的虛擬環境中模擬與測試 AI 驅動的機器人。
NVIDIA 今日在 AWS re:Invent 大會中宣布，現在可以在由 NVIDIA L40S GPU 加速的 Amazon Elastic Cloud Computing（EC2）G6e 執行個體上執行 Isaac Sim。開發人員還能透過雲端原生的協調平台
NVIDIA OSMO
，在 AWS 運算基礎架構中輕鬆管理複雜的機器人工作流程。
可在雲端使用的 NVIDIA 加速硬體與軟體組合，可讓任何規模的團隊擴充其實體 AI 工作流程。
實體 AI
描述了能夠理解實體世界並與其進行互動的 AI 模型。它體現了
自主機器和機器人
的下一波發展浪潮，如自駕車、工業機械手、移動機器人、人形機器人，甚至是機器人管理的基礎設施，如工廠和倉庫。
有了實體 AI，開發人員正採用
三電腦解決方案（three computer solution）
進行訓練、模擬和推論，以求突破。
然而，機器人系統的實體 AI 需要強大的訓練資料集，才能在部署環境裡取得精確的推論結果。不過想要開發這樣的資料集，並在實際環境裡進行測試，既不實際且成本又高。
模擬提供了答案，因為這項技術可以顯著加快 AI 驅動機器人的訓練、測試和部署。
在雲端運用
L40S GPU
來擴大模擬與訓練機器人的規模
模擬可在部署前用於確認、驗證和最佳化機器人設計，以及相關系統及其演算法。模擬還能在施工或改造開始前最佳化設施和系統設計，以達到最高效率，避免在製造過程中因變更訂單而產生的高昂成本。
由 NVIDIA L40S GPU 加速的 Amazon EC2 G6e 執行個體，提供比先前架構高出兩倍的效能提升，同時還能隨著場景及模擬複雜度增加而擴充的彈性。這些執行個體用於訓練許多為 AI驅動機器人提供動力的電腦視覺模型。這意味著相同的執行個體可以擴充來執行各種任務，從資料生成到模擬，再到模型訓練。
在雲端使用
NVIDIA OSMO
，可以讓團隊對分散各處的運算資源，無論是在本地或 AWS 雲端，都能協調與擴充複雜的機器人開發工作流程。
Isaac Sim 讓使用者可以獲得最新的
機器人模擬功能
及雲端資源，以促進合作。其中一個關鍵的工作流程是產生訓練感知模型所需的合成資料。
開發人員使用結合
NVIDIA
Omniverse
Replicator
與
NVIDIA NIM 微服務
的
參考工作流程
，便能建立支援生成式 AI 的 SDG 管道。NVIDIA Omniverse Replicator 是用於建立自訂合成資料生成（SDG）管道的框架，以及 Isaac Sim 的核心擴充功能。
其中包括用於產生 Python USD 程式碼和回答 OpenUSD 查詢的 USD Code NIM 微服務，以及用於使用自然語言或圖像輸入探索 OpenUSD 資產的 USD Search NIM 微服務。Edify 360 HDRi NIM 微服務可產生 360 度環境地圖，而 Edify 3D NIM 微服務則可根據文字或影像提示，建立可立即編輯的 3D 資產。如此一來便能利用生成式 AI 的力量，減少從建立資產到增強影像等許多繁瑣的手動步驟，簡化生成合成資料的流程。
Rendered.ai
的合成資料工程平台與 Omniverse Replicator 整合後，可讓企業為安全、情報、製造到農業等產業所使用的電腦視覺模型產生合成資料。
IT 諮詢與數位服務供應商
SoftServe
使用 Isaac Sim 來產生合成資料，且與歐洲領先的食品生產商 Pfeifer & Langen 合作驗證垂直農業中使用的機器人。
塔塔顧問服務（Tata Consultancy Services）建立客製化的合成資料生成管道，驅動其 Mobility AI 套件，藉由模擬真實世界的情境來解決汽車與自動化使用個案。其應用包括瑕疵偵測、生產線末端品質檢查及避免危險情況。
在模擬環境中學習成為機器人
Isaac Sim 可讓開發人員在精準符合物理原則的模擬環境中測試及驗證機器人，而建立在 Isaac Sim 上的開源機器人學習框架
Isaac Lab
則為建立可在 AWS Batch 上執行的機器人政策提供虛擬空間。
由於這些模擬是可以重複的，因此開發人員可以輕鬆排除故障，減少驗證和測試所需的週期。
多家機器人開發業者在 AWS 上採用
NVIDIA Isaac
來開發實體 AI。
Aescape
的機器人能夠透過 Isaac Sim 中對機器人身上的感應器進行準確的建模及調整，提供精準且量身打造的按摩服務。
Cobot 已將 Isaac Sim 與其 AI 驅動的協作機器人 Proxie 搭配使用，以最佳化倉庫、醫院、製造場所等地的物流作業化。
Cohesive Robotics 已將 Isaac Sim 整合至其名為 Argus OS™ 的軟體框架，用於開發和部署在高混合製造環境裡使用的機器人工作單元。
機器人基礎模型建造商的 Field AI 使用 Isaac Sim 和 Isaac Lab 來評估其模型在複雜、非結構性環境下的效能表現，這些環境涵蓋建築、製造、石油和天然氣、採礦等產業。
Standard Bots
正在模擬和驗證其用於製造和加工設置之的R01 機器人效能。
Swiss Mile
正在使用 Isaac Sim 和 Isaac Lab 進行機器人學習，使輪型四足機器人能夠在工廠和倉庫裡，以更高的效率自主執行各項任務。
Vention
提供基於雲端的全端自動化平台，正在使用 Isaac Sim 開發和測試中小型製造商使用的機器人單元新功能。
了解更多關於 Issac Sim 4.2 資訊，Issac Sim 4.2 現已在
AWS Marketplace
上由 NVIDIA L40S GPU 驅動的 Amazon EC2 G6e 執行個體上提供。
Categories:
自主機器
Tags:
NVIDIA Isaac Sim
|
Omniverse Enterprise