Robotics startups are boosting their capabilities with the help of NVIDIA’s Isaac Sim, leveraging the cloud-based technologies offered by Amazon Web Services (AWS).
Prominent names such as Field AI, Vention, and Cobot are harnessing these tools to push the boundaries of technology—enabling innovations in fields ranging from industrial processes to collaborative robots.
Field AI focuses on developing foundational robotic brains capable of autonomously managing complex industrial workflows. Vention simplifies robotic task development by providing pre-trained skills, while Cobot has unveiled Proxie, an AI-driven collaborative robot designed to seamlessly navigate dynamic environments alongside humans.
The shared core of these advancements is NVIDIA’s Isaac Sim, a reference application powered by NVIDIA Omniverse for the simulation and testing of AI-enabled robots in hyper-realistic, virtual environments. The platform is now bolstered by new cloud-powered performance enhancements.
AWS and NVIDIA GPUs: A powerful combo for Isaac Sim innovation
Unveiled during the AWS re:Invent event, NVIDIA announced the deployment of Isaac Sim on Amazon Elastic Cloud Computing (EC2) G6e instances, accelerated by NVIDIA’s latest L40S GPUs. These instances deliver twice the computational performance of previous generations and offer increased flexibility as developers tackle increasingly complex robotic use cases.
Coupled with NVIDIA OSMO – a cloud-native orchestration platform – developers can streamline and scale the intricate workflows involved in robotics development, whether using AWS cloud infrastructure or on-premises systems.
This unified framework of high-performance hardware and software transforms robotics innovation for teams both large and small, empowering the development of “physical AI.”
Physical AI, an emerging concept, describes intelligent models trained to comprehend and interact with the physical environment—driving advancement in autonomous machines like humanoid robots, self-driving vehicles, and industrial systems.
The role of simulation in robotics development
Robotics infrastructure hinges on robust training data to ensure precision in physical AI models. Collecting such datasets from real-world environments is often cost-prohibitive and logistically challenging. Virtual simulations, however, offer a practical solution, accelerating AI model training while optimising processes before real-world deployment.
Simulation plays a pivotal role in validating robotics systems, testing facility designs, and developing computer vision AI models.
Amazon EC2 G6e instances, supported by NVIDIA’s L40S GPUs, are crucial in this workflow—with enhanced capabilities for data generation, simulation, and model optimisation. These solutions allow developers to improve operational efficiencies while minimising costly errors during both the design and implementation phases.
By integrating NVIDIA OSMO into cloud workflows, teams can synchronise their robot development projects, leveraging solutions like Isaac Sim for cutting-edge collaboration. Developers can generate synthetic data, create 3D assets, and streamline workflows using NVIDIA Omniverse Replicator, a framework that integrates generative AI tools.
Generative AI meets robotics training
The ability to generate synthetic data has emerged as a cornerstone of accelerated robotics development.
NVIDIA’s solutions use generative AI for creating data-driven workflows, encompassing tools like USD Code NIM for Python scripting and 3D asset manipulation, Edify HDRi for generating virtual environments, and Edify 3D for crafting edit-ready 3D object files from text or image prompts.
Rendered.ai, for instance, employs Omniverse Replicator to create synthetic computer vision datasets, serving industries from manufacturing and agriculture to surveillance and security. Similarly, Tata Consultancy Services is using synthetic data pipelines to power its Mobility AI suite, which focuses on automotive use cases ranging from defect detection to hazard avoidance.
This technological leap reduces tedious manual steps, enables scalable data production, and fosters the development of robotics systems capable of tackling increasingly sophisticated challenges.
Real-world use cases leveraging Isaac Sim
A growing number of startups and enterprises are capitalising on Isaac Sim’s simulation and synthetic data capabilities to enhance robotics projects.
For example, Aescape leverages Isaac Sim to fine-tune sensors for robots used in delivering precision massages. Similarly, Cobot has used Isaac Sim to optimise its logistics-focused collaborative robot, Proxie, which is suited for dynamic industries such as manufacturing, healthcare, and warehousing.
Other notable adopters of Isaac Sim include:
- Field AI: Employs Isaac Lab, an open-source robot learning platform, to test robotic systems in unstructured environments in industries like construction, manufacturing, and mining.
- Vention: Utilises Isaac Sim to develop robotic cell capabilities for small and medium-sized manufacturers.
- Swiss Mile: Incorporates Isaac Lab and Isaac Sim for robotics learning, enhancing the capabilities of wheeled quadruped robots in navigating factory and warehouse tasks.
- Standard Bots: Simulates and validates its R01 manufacturing robot’s performance under real-world conditions.
- SoftServe: Collaborates with food producer Pfeifer & Langen to develop farming robots optimised for vertical setups.
- Cohesive Robotics: Integrates Isaac Sim into its Argus OS software, which powers robotic workcells for high-variation manufacturing systems.
The repeatability and control of virtual learning environments significantly reduce hardware testing cycles, while ensuring deployment-ready solutions emerge faster.
By combining NVIDIA Isaac Sim with AWS’ cloud ecosystem, developers worldwide are advancing robotics innovation. This integration of high-performance simulation, generative AI tools, and scalable infrastructure ensures robotics can tackle increasingly complex scenarios in diverse environments.
Robotics systems are set to undergo rapid evolution, with fewer limitations in testing, validation, and optimisation. As a result, experts anticipate an acceleration of physical AI applications.
(Image Credit: NVIDIA)
See also: Figure 02: A leap forward in humanoid robotics
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