I’m thrilled to sit down with Dominic Jainy, a seasoned IT professional whose deep expertise in artificial intelligence, machine learning, and blockchain also extends into the innovative realm of robotics simulation. Today, we’re diving into his insights on the Newton Project, a groundbreaking open-source initiative that’s pushing the boundaries of physics-based simulation with GPU-powered computing. Our conversation explores how Newton is transforming robotics development, its unique architecture, the role of cutting-edge frameworks, and its mission to make robotics innovation faster and more accessible through community-driven efforts.
How did the Newton Project come about, and what drove it to join forces with the Linux Foundation?
The Newton Project was born out of a need to create a powerful, open-source platform for physics-based simulation in robotics. It’s all about giving developers the tools to build and test robotic systems in a virtual environment before they hit the real world. Joining the Linux Foundation was a natural step because it’s a hub for collaborative, community-focused projects. The Foundation provides a structure for governance, training, and partnerships that help us scale our reach. It aligns perfectly with our goal of democratizing access to advanced simulation tools and fostering global innovation through shared code and resources.
What’s the core mission of Newton in advancing robotics simulation?
At its heart, Newton aims to accelerate the development of general-purpose robotics by providing a scalable, GPU-enhanced simulation engine. We want to cut down the time and cost of building robots by allowing developers to test designs and behaviors in a virtual space. This not only speeds up the iteration process but also makes robotics solutions more affordable. By leveraging open-source principles, we’re ensuring that anyone—from startups to large enterprises—can access high-quality tools to create optimized, real-world-ready systems without breaking the bank.
Can you walk us through the key pieces of Newton’s architecture and how they work together?
Sure, Newton’s architecture is designed for flexibility and precision. It starts with ModelBuilder, which lets developers create detailed robot models by importing assets and defining parameters like mass, inertia, or collision properties. Then there’s State, which tracks all the dynamic data that changes during a simulation, like position or velocity. The Solver is where the magic happens—it integrates physics to make the simulation realistic, solving for how a robot moves under specific conditions. Finally, the Viewer component helps developers visualize the simulation, either in real-time or after the fact, making it easier to debug and refine. Together, these components create a seamless pipeline for building and testing robotic systems.
I’ve heard Newton taps into GPU power with NVIDIA Warp. How does this enhance what the platform can do?
GPU acceleration through NVIDIA Warp is a game-changer for us. Warp allows developers to write low-level code in Python that runs in parallel on GPUs, speeding up simulations dramatically. With what we call Warp Kernels, we can execute thousands of simulations simultaneously—sometimes tens of thousands per second. This is huge for AI training in robotics, where you need massive amounts of data to teach models how to handle different scenarios. The sheer speed and scale mean developers can iterate faster and train more robust AI systems in a fraction of the time it would take with traditional CPU-based methods.
Newton offers a range of solvers like Euler and Featherstone. Why is having this variety so valuable for developers?
Having multiple solvers gives developers the freedom to tailor simulations to their specific needs. Each solver has strengths—Euler might be ideal for simpler, basic testing, while Featherstone excels with rigid-body systems like robot arms or humanoids. On the other hand, XPBD is great for soft robotics or real-time applications. This variety lets users pick the best tool for the job or even combine solvers for hybrid approaches. Since the Solver is decoupled from the Model in our design, swapping them out is easy, which saves time and encourages experimentation across diverse robotics projects.
Could you explain the role of the Open USD framework in Newton and why it matters?
Open USD is a critical piece of Newton’s ecosystem. It’s a standardized format for importing models into our platform, ensuring that physical properties, geometry, and other key parameters are preserved consistently. This standardization makes it easier for developers to work across different tools and platforms without losing data integrity. It streamlines workflows, boosts compatibility with other simulation or graphics software, and ultimately saves developers from the headache of reformatting or rebuilding models. It’s all about making the process smoother and more collaborative.
What’s your forecast for the future of open-source robotics simulation with initiatives like Newton leading the way?
I’m incredibly optimistic about where this is headed. With projects like Newton, backed by communities like the Linux Foundation, I see open-source robotics simulation becoming a cornerstone of innovation in the field. We’re likely to see even faster adoption of simulation tools as they become more accessible and powerful, especially with advancements in GPU computing and AI integration. I think we’ll witness a surge in creative, affordable robotics solutions across industries—from manufacturing to healthcare—as more developers and companies tap into these resources. The collaborative nature of open-source will only amplify this, breaking down barriers and driving progress at an unprecedented pace.