Antioch raises $8.5 million to build simulation tools that accelerate physical AI development

This simulation startup wants to be the Cursor for physical AI | TechCrunch

The promise of physical AI is that engineers will be able to program physical agents the same way they do digital ones. We're not there yet. Robotics is still held back by a paucity of data from physical spaces. To train their machines, companies need to build mock-up warehouses to test their machines, while an entire industry is springing up around surveilling factory lines and gig workers to train deep learning models to operate robots. Another option is simulation; detailed virtual replicas of real-world environments could provide the data and workspaces that roboticists need to do this work in a scalable way. Antioch, a startup building simulation tools for robot developers, wants to close what the industry calls the sim-to-real gap -- the challenge of making virtual environments realistic enough that robots trained inside them can operate reliably in the physical world. "How can we do the best possible job reducing that gap, to make simulation feel just like the real world from the perspective of your autonomous system?" Antioch CEO and cofounder Harry Mellsop said. To do that, the company told TechCrunch today that it has raised an $8.5 million seed round that values it at $60 million, led by venture firm A* and Category Ventures, with additional participation from MaC Venture Capital, Abstract, Box Group, and Icehouse Ventures. Mellsop started the New York-based company with four cofounders in May of last year. Two of the other founders, Alex Langshur and Michael Calvey, helped him found Transpose, a security and intelligence startup, and sell it to Chainalysis for an undisclosed amount. The other two -- Collin Schlager and Colton Swingle -- previously worked at Google DeepMind and Meta Reality Labs, respectively. The need for better simulation is at the heart of what many major autonomy companies are doing. In the self-driving car space, for example, Waymo uses Google DeepMind's world model to test and evaluate its driving model. In theory, that technique will make deploying Waymo vehicles in new areas require less data collection, a key cost in scaling up autonomous vehicle technology. Building and using those models to test robots is arguably a different set of skills than creating a self-driving car, and Antioch wants to build the platform that solves that problem for newer companies without the capital to do it all themselves. Those smaller companies also don't have the capital to build physical testing arenas or drive sensor-studded cars for a few million miles. "The vast majority of the industry doesn't use simulation whatsoever, and I think we're now just really understanding clearly that we need to move faster," Mellsop said. Antioch executives compare their product to Cursor, the popular AI-powered software development tool. Antioch allows robot builders to spin up multiple digital instances of their hardware and connect them to simulated sensors that mimic the same data the robot's software would receive in the real world. These environments allow developers to test edge cases, perform reinforcement learning, or generate new training data. If, that is, the simulation is sufficiently high fidelity. The challenge here is making sure the physics in the simulation matches reality so that when the model is put in charge of a real machine, nothing goes wrong. The company starts with models built by Nvidia, World Labs, and others, and builds domain-specific libraries to make them easy to use. Working with multiple customers, executives say, gives Antioch a depth of context for refining its simulations that no single physical AI company could match on its own. "What happened with software engineering and LLMs is just starting to happen with physical AI," Çağla Kaymaz, a partner at Category Ventures, told TechCrunch. "We do a lot of work on dev tools, and we love that vertical, but the challenges are different. With software, you can have these bad coding tools, and the risk is generally pretty contained to the digital world. In the physical world, the stakes are much higher." Antioch's focus now is mainly on sensor and perception systems, which account for the bulk of the need in automated cars and trucks, farm and construction machinery, or aerial drones. Aspirations for physical AI to power generalized robots to replicate human tasks are further away. While Antioch's pitch is to startups, some of its earliest engagements have been with huge multinationals that are already investing heavily in robotics. Adrian Macneil has a solid understanding of this space. As an executive at the self-driving startup Cruise, he built the company's data infrastructure, and in 2021 founded Foxglove, a company that offers the same kind of data pipelines to physical AI startups. Macneil is backing Antioch as an angel investor. "Simulation is really important when you're trying to build a safety case or dealing with very high-accuracy tasks," he said at the Ride.AI conference in San Francisco on Wednesday. "It's not possible to drive enough miles in the real world." Macneil would like to see the same kind of tools that drove the SaaS revolution -- platforms like Github, Stripe, and Twilio -- emerging to support physical AI. "We need a lot more of the entire toolchain to be available off the shelf," he told TechCrunch. "We genuinely all think that anyone building an autonomous system for the real world is going to do so in software primarily in two to three years," Mellsop said. "It's the first time you can have autonomous agents iterate on a physical autonomy system, and actually close the feedback loop." There are already experiments in this direction. David Mayo, a researcher at MIT's Computer Science and Artificial Intelligence Laboratory, is using Antioch's platform to evaluate LLMs. In one experiment, Mayo has AI models design robots, then use Antioch's simulator to test them. It can even pit the models against each other in simulated contests, like pushing a rival bot off a platform. Giving the LLMs a realistic sandbox could help provide a new paradigm for benchmarking them. Before a world of AI engineers arrives, however, there is still more work ahead to close the gap between the digital models and the real world. If it can be done, developers will be able to create the kind of data flywheel that Macneil believes is the key to the success of category leaders like Waymo, where engineers are increasingly confident that next month's model will be more capable than the last. If other companies want to replicate that success, they'll need to build those tools themselves -- or buy them.

Antioch prepares to accelerate simulated testing for autonomous robots after raising $8.5M - SiliconANGLE

Antioch prepares to accelerate simulated testing for autonomous robots after raising $8.5M Antioch Inc., a developer of cloud-based simulation software for artificial intelligence-enabled robots, has raised $8.5 million in funding to accelerate the development of more autonomous systems outside the physical world. Today's round, which comes just four months after the startup raised $4.5 million in pre-seed funding, was led by A* and Category Ventures. Others, including MaC Venture Capital, Abstract, Box Group and Icehouse Ventures also participated, as did angel investors including Shyam Sanker of Palantir Technologies Inc. and Adrian Macnei of Foxglove Inc. Antioch's mission is to replace the need for robotics companies to conduct testing in the physical world using real hardware and elaborately prepared spaces where it can be put through its paces. Generally, validating that robots behave as they're designed to in the real world is extremely expensive and time-consuming. It requires developers to rent and prepare a physical space with different staging environments, and then resetting the hardware after every test run. The process is both expensive and cumbersome, and only ever covers a small fraction of the scenarios most robots will face once they're deployed in real-world production environments. That's why simulation can be so powerful. By using a combination of physics and rendering engines with the most advanced "world models," it's possible to create digital twins of robots and simulate realistic environments that can be used for testing instead. But the problem is that this infrastructure is evolving far too rapidly for most developers to keep up, with new models and software tools constantly emerging with new capabilities. Antioch's role is to build the "platform layer" that connects developers of autonomous machines to this wave of constant innovation. Co-founder and Chief Executive Harry Mellsop (pictured, far left), who previously helped develop Tesla Inc.'s Autopilot software, compares his company to Cursor, the generative AI coding platform that makes the latest AI models instantly accessible to software developers. Its goal is to do the same for "autonomy teams" by ensuring they're always able to access the most advanced simulation and infrastructure tools for testing robots. More specifically, Antioch has developed a cloud simulation platform that enables teams to create digital twins of any kind of robot, so they can quickly validate any AI-powered system before it's deployed in the real world. The goal is to eliminate the testing cycles that slow down robotic development. So instead of spending hours resetting a robot or moving it from one place to another, developers can just click a few buttons to get ready for the next test. Moreover, they can carry out thousands of tests in parallel by spinning up multiple digital twins of their robots at once. "Robotics teams spend weeks staging warehouses and investing millions into test facilities to validate their systems," Mellsop told SiliconANGLE. "Meanwhile, companies like Tesla, Waymo and Anduril spend hundreds of millions a year on simulation infrastructure to minimize exactly that. We think every autonomy team should have access to that level of tooling." Mellsop and two of his co-founders, Alex Langshur (second from right) and Michael Calvey, previously founded a security and intelligence startup called Transpose Inc. that was acquired by Chainalysis Inc. in 2023. That company grew to serve a number of U.S. intelligence and law enforcement agencies, and the co-founders believe that they're once again serving America's best interests with Antioch. Langshur explained that America used to have a significant manufacturing advantage over the rest of the world, only to throw it away through offshoring. As a result, it's no longer the manufacturing capital of the world, and that's something the current U.S. administration is trying to address. "The only economically viable path to reindustrialization runs through robotics and automation, and scalable testing is the rate-limiting step," he insisted. Mellsop told SiliconANGLE that the potential of doing this far exceeds even the AI industry that is becoming critical to robotics and automation today. He points out that the industries currently being disrupted by AI, such as software, professional services and knowledge work, represent around $8 trillion of the global economy. "But manufacturing, logistics, construction, energy and agriculture represent more than $50 trillion," he said. "AI penetration in those industries is basically zero today. The industrial revolution that's coming with physical AI won't be a sequel to the LLM revolution, it will dwarf it."