One Robot
One Robot builds simulation environments that are realistic to see and realistic to interact with, so robotics teams can train and evaluate robot policies without being bottlenecked by robot time. Today, improving a VLA often means more real-world hours: setting up the scene, running trials, resetting, and repeating. This loop is slow, expensive, and hard to scale. For example, material handling and manufacturing assembly tasks, models need far more training and evaluation data than teams can co
Y Combinator
yc-company-directory #6007One Robot builds simulation environments that are realistic to see and realistic to interact with, so robotics teams can train and evaluate robot policies without being bottlenecked by robot time. Today, improving a VLA often means more real-world hours: setting up the scene, running trials, resetting, and repeating. This loop is slow, expensive, and hard to scale. For example, material handling and manufacturing assembly tasks, models need far more training and evaluation data than teams can co
sourceOne Robot is classified as AI infrastructure from source descriptions and fund-directory context. Similar companies below are ranked from offline product facets and meaningful description overlap; category and source affiliation can only strengthen an existing product match. For lookup-created rows, the profile starts as pending enrichment until deeper source collection runs.
Local clusters rerank extracted product concepts and description terms. The generated graph remains the fallback for sparse descriptions: offline reciprocal product-similarity graph: extracted concepts + description terms + bounded category/affiliation boosts. Minimum fallback score: 0; max competitors: 6.
Companies similar to One Robot
A deterministic local cluster built from offline product facets and meaningful description terms. Category and selected-institution affiliation provide bounded tie-breaks. Open any peer to continue exploring its cluster.
Cortex AI
match 78Cortex AI builds the world’s most diverse and large-scale real-world workplace robot & egocentric dataset — where the physical world becomes the next training and evaluation set for embodied AI. We power frontier labs developing robotics foundation models and general-purpose robots by providing the data they need: 1️⃣ Egocentric Data — real-workplace human video with hand/body pose, depth, and subtask labels. 2️⃣ Robot Data — trajectories collected from manipulators and humanoids in real industr
shared extracted concept terms: robot; shared product terms: scale, real, robot, train; same category: AI infrastructure; shared affiliation: Y Combinator
Twolabs
match 70We build modular humanoid robots and the software platform that allows anyone to train, deploy, and scale robots for real-world tasks. Today, teaching a robot a new skill requires robotics engineers, machine learning engineers, training infrastructure, and significant compute resources. We reduce that process to: Record → Upload → Train → Deploy. Our robots are modular and configurable, with interchangeable end effectors, configurable sensors, and multiple deployment configurations. We believe c
shared extracted concept terms: robot; shared product terms: robot, robotic, train, today; same category: AI infrastructure; shared affiliation: Y Combinator
Piggy Robotics
match 65We build humanoid robots that do your chores at iPhone prices. Just like the millions of cars generating data for self-driving, we need millions of humanoids generating real-world data for general-purpose robotics. Humanoid companies today can only manufacture hundreds a year because motors are expensive and slow to assemble. We use artificial muscles. Each muscle is just a tube wrapped in braided fibre, and the whole robot is powered by a single pump — which means we can mass-produce humanoids
shared product terms: robot, robotic, today, mean; shared affiliation: Y Combinator
Halluminate
match 65We help foundation model labs and enterprises train computer use AI with data and sandboxes. A knowledge worker spends 90% of their day on a computer. A productive AI agent must similarly learn how to use our computers, browsers, and software to deliver real world value. Today, browser and computer use AI is inaccurate, slow, and expensive. Model labs and enterprises who want to improve these agents are bottlenecked by two resources: 1) High quality datasets for benchmarking and evaluations 2) R
shared product terms: model, train, real, today; shared affiliation: Y Combinator
Physical Turing
match 62Physical Turing tests humanoids in the real world and returns actionable failure data before deployment. We procure, staff, and operate a wide range of target environments for real-world rollouts, helping robot makers, model companies, and enterprises continuously evaluate where humanoids succeed, where they fail, and what needs to improve before each deployment.
shared extracted concept terms: robot; shared product terms: robot, environment, evaluate, real; same category: AI infrastructure; shared affiliation: Y Combinator
Autotab
match 60Autotab is a drop-in AI knowledge worker that uses a mouse and keyboard like you—so anything you can do, it can do. Show it how to perform your task, train it to be hallucination-proof by adding examples and providing feedback, and then run it on demand or on a schedule—either locally or in the cloud. It achieves superhuman reliability, and can scale up on demand so your team doesn’t have to. Autotab is already handling complex tasks thousands of steps long, from messaging customers to triggerin
shared product terms: so, task, train, example; same category: AI infrastructure; shared affiliation: Y Combinator