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This simulation startup wants to be the Cursor for physical AI
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. Techcrunch event San Francisco, CA | October 13-15, 2026 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 ...
IBM's Martin Keen on Physical AI Training - startuphub.ai
Martin Keen, a Master Inventor at IBM, recently shared insights into the evolving field of Physical AI, explaining its core concepts and the challenges in its development. Keen articulated that while much of today's AI operates within the digital realm of 'bits,' Physical AI aims to bridge this to the physical world of 'atoms.' This new frontier involves AI systems that can not only process information but also interact with and influence their physical environment. Understanding Physical AI Keen defines Physical AI as AI systems capable of perceiving their environment, reasoning about it, and taking actions within it. These are not just abstract models but agents that can, for example, manipulate objects, navigate complex spaces, or even perform tasks in manufacturing or logistics. He draws a parallel to current AI applications like chatbots or image generators, which operate purely in the digital space, distinguishing them from the tangible interactions of Physical AI. The Rise of robotic AI agents Keen highlights that the development of physical AI is closely tied to the creation of 'robotic AI agents.' These agents are characterized by their ability to learn and adapt. Unlike traditional robots with fixed, rule-based behaviors, these AI-powered agents can acquire new skills and improve their performance through experience. This learning process is often a combination of general understanding derived from large datasets and specific skill acquisition through methods like reinforcement learning.The full discussion can be found on IBM's YouTube channel. What is Physical AI? How Robots Learn & Adapt in Real Life — from IBM Bridging the Simulation-Reality Gap A significant challenge in training Physical AI is the discrepancy between simulated environments and the complexities of the real world. Keen explains that simulations are crucial for generating vast amounts of training data efficiently. However, simply training in a perfect simulation often leads to models that perform poorly when deployed in the messy, unpredictable physical world. To address this, the concept of 'domain randomization' is employed, where simulations are intentionally varied with different parameters—such as lighting, friction, and object properties—to expose the AI to a wider range of conditions. This process helps the AI learn to generalize better and transfer its learned skills to real-world scenarios. The Role of Compute Efficiency Keen emphasizes that the progress in Physic...
Accenture Invests in General Robotics to Advance Physical AI-Powered ...
General Robotics brings GRID, a unified intelligence platform that connects robots across robotics original equipment manufacturers to deployable, scalable, and adaptable AI. Rather than relying on static programming, the platform focuses on modular, reusable AI skills, cloud-based orchestration, simulation training and sovereignty over data and intellectual property. Accenture brings deep ...
Accenture Invests in General Robotics GRID Platform
Accenture Ventures has invested in General Robotics to commercialize the company's `GRID` platform, a unified intelligence layer that deploys modular AI skills across **40+ robots** from vendors such as **FANUC**, **Flexiv**, **Ghost Robotics**, **Galaxea**, and **Psyonic**. The platform emphasizes cloud-based orchestration, simulation-based training using `Isaac Sim`, and enterprise data ...
