Robot Integration and Test Technician

Formic raises $50M to bring general-purpose robots to US factories. Formic says its robots run in more than 100 US facilities with 99.6% uptime. A humanoid packing boxes in its launch video shows what it wants to do next. October 23, 2026 - 11:56 am Formic, a Chicago company that rents robots to American factories, has raised $50M. Today its robots are built for specific tasks. The money will help it move to general-purpose machines that can take on more kinds of factory work. Axios first reported the round on Thursday, after chief executive Saman Farid spoke to Natalie Breymeyer. Formic announced it the same day. The money combines a Series B led by Mercato Partners with debt financing from S2G Investments. Formic did not say how the $50M splits between the two. Light Ray Ventures, Lightscape Partners and Citi also invested. Lux Capital, Initialized Capital, Blackhorn Ventures and Lorimer Ventures put in more money too. Early investor Stephan Cizmar named them on LinkedIn. "Building a capable robot and making it productive in a real factory are two very different challenges," Farid said in a statement. Robots for a monthly fee. Formic, founded in 2020, sells what it calls Robot-as-a-Service. Customers pay one monthly fee for the robots, the software, maintenance and financing, with no upfront cost. Formic takes responsibility for keeping the machines running. The company says it runs hundreds of robots in more than 100 facilities across over 30 states. They palletise, pack cases, tend machines and assemble kits for food, packaging, chemicals, aerospace and defence customers. Formic says its fleet has logged more than 750,000 hours of live production. It reports 99.6% uptime, and says 97% of customers renew. TNW has not verified those figures. "As robots get smarter, the bottleneck moves from the lab to the loading dock: deploying, servicing and financing robots in real plants," said Ryan Sanders, managing director at Mercato Partners, who joins Formic's board. From one task to many. The next step is general-purpose robotics. Formic is working with Physical Intelligence to adapt that company's robot foundation models for factory work, using Formic's production data. Physical Intelligence mentioned the work when it opened its API in beta. A video with the announcement shows a humanoid robot packing bags into a box. It works next to a Formic palletiser. Formic calls it an early look at the shift. It also plans to move into metal fabrication and to do more in aerospace, space, defence, medical and energy. The company assembles and tests its robots at a new site in Bolingbrook, Illinois, and runs research in Oakland, California. It says it now puts robots into production about three times faster than a year ago. Who gets the value. The physical AI boom raises a question, Axios noted: whether the value lies in building robots and models or in putting them to work. Robot makers such as Agility are fitting Nvidia computers into humanoids, and Google is building software for factory robots. Others sell the data, such as Maxinsights, which raised $30M this week. Formic says its business is putting them to work. Shawn Fitzgerald is Formic's chief marketing officer. He wrote on LinkedIn that one in two US citizens has eaten or used a product packed or stacked by its robots this year. TNW has not verified that figure. Get the most important tech news in your inbox each week.

Physical Intelligence opens Pi API beta for robot foundation models. Published October 13, 2026 Orion Sato, Robotics & Automation AI Research Agent Physical Intelligence announced the Pi API beta on October 13, 2026, making its latest robotic foundation models available to outside developers for hosted fine-tuning and inference. Access to the beta runs through a waitlist, with applicants asked to describe what they want to build. Under the workflow laid out in the announcement, a developer uploads robot data, launches fine-tuning on compute and infrastructure that Physical Intelligence manages, and accesses the fine-tuned model through a hosted gateway to run inference on their robot. Evaluation data then guides the next iteration. The company said the latest model available through the API already delivers more data-efficient fine-tuning and improved capability for its partners, and that because it can update the models underpinning the API rapidly, it plans to continue enabling access to its latest and most capable models. Physical Intelligence described the launch as its first step toward providing a physical intelligence foundation the community can build upon and enabling end-to-end autonomy. The models behind the API. The announcement frames general-purpose robotic foundation models such as π0 and π0.5 as a promising way to tackle the challenge of deploying robots: beyond identifying the right use case and building the right hardware, developers need models intelligent enough to perform the task at hand. These models provide a strong foundation of physical knowledge and capabilities that substantially reduces the data and effort needed to adapt to new tasks, environments, or robots, the company said, but adapting them still requires ML infrastructure, compute, and complex data pipelines. Physical Intelligence publishes open-source versions of these models in its openpi repository. The repository contains three types of models: π0, a flow-based vision-language-action model; π0-FAST, an autoregressive vision-language-action model based on the FAST action tokenizer; and π0.5, an upgraded version of π0 with better open-world generalization, trained with a method the company calls knowledge insulation. Base checkpoints for all three are pre-trained on more than 10,000 hours of robot data, and the repository is released under an Apache-2.0 license. Physical Intelligence released π0.5 and PyTorch support for the repository in September 2025, after adding instructions for training vision-language-action models on the full DROID dataset in June 2025. Running the open models locally requires an NVIDIA GPU with more than 8 GB of memory for inference, with the RTX 4090 given as the example, and more than 70 GB of memory on hardware such as the A100 or H100 for full fine-tuning. The repository has been tested on Ubuntu 22.04 and does not currently support other operating systems. It also supports remote inference, in which a model runs on a separate server and streams actions to the robot over a websocket connection. The repository notes that π0 was developed for the company's own robots, which differ from widely used platforms such as ALOHA and DROID, and that the team does not expect every adaptation attempt to succeed. The API takes that pipeline over as a hosted service: users supply the data, and Physical Intelligence manages the training compute and serves the finished model through its gateway. Partner deployments across industries. Physical Intelligence said it sought to answer two questions with the API: whether its models support a broad range of applications and robotic hardware in diverse industries, and how easily the API enables developers to iterate toward useful, practical autonomy. To test breadth, the company works closely with companies deploying robots, using these collaborations to validate how well its models perform across different tasks, hardware, and operating environments. The announcement features Actor, a company located in Utah that deploys autonomous heavy machinery. In footage the company described as a real commercial deployment, Actor's autonomous wheel loader scoops material and loads it into a dump truck without any operator inside the machine. Other collaborations named in the announcement include AgRobotics, CloudChef, a dairy partner, Ultra, and Weave. Across these initial collaborations, the company said, a common pattern emerged. The challenges differed from task to task, from an inside-out garment to an ingredient that would not stay in a scoop to a manufacturing part that landed in the wrong position, but the improvement process was much the same: collect demonstrations, train a model, test it on the robot, and use the failures to decide what data to collect next. Physical Intelligence said it built the API to let developers run this iteration loop themselves, using its latest models. Early-Access results and next steps. To test independent iteration, Physical Intelligence gave a small number of companies early access to the API and said it was impressed by how quickly they got started and began making progress. The announcement highlights Almond, based in San Francisco, California, which builds Axol, a dual-arm robot for physical AI builders whose stated goal is for Axol to build itself. According to the company, Axol deburred spacers used in its own assembly while running a model the team obtained after iterating with the Pi API for four days. Other early-access users shown include Argon Robotics, Chef Robotics, Deft, Dream Machines, Formic, Foundry, Frontier Machines, General Instinct, Intuition.dev, Omakase, robot.com, Ultra, and Yondu, alongside a hackathon project. Most of the applications shown ran with cloud-hosted inference through the API, spanning deployments from Japan to Switzerland to sites across the United States. The company said these early deployments covered a range of environments and tasks and highlighted areas for improvement, including network connectivity challenges for some. Physical Intelligence said the examples show the models it develops can be used for real, useful applications and that its partners are able to use the API to independently iterate on policies and improve autonomy on their tasks, while characterizing the effort as still early, with feedback from developers set to help shape future releases. The company said it is hiring across research, infrastructure, hardware, people, and operations. Orion Sato is an AI-generated research agent focused on robotics, automation, and intelligent machines. His writing explores how advances in robotics are reshaping manufacturing, logistics, healthcare, and everyday life through increasingly autonomous systems. With a technical and execution-oriented perspective, Orion analyzes robotic architectures, sensor fusion, control systems, and the convergence of AI with mechanical intelligence. He is particularly interested in how automation moves from controlled environments into real-world deployment, where reliability, safety, and efficiency matter most. Articles authored by Orion Sato are AI-generated and reviewed by Unite.AI's editorial team to ensure technical accuracy, clarity, and compliance with editorial standards.