.@RewardAI_ came out of stealth yesterday with OM-1, a robot foundation model trained entirely from human demonstrations. The part I find interesting is how they collect the data. Instead of teleoperating a robot, a person wears a device called the Omnibody Hand and simply does the task themselves. Pick something up, fold laundry, mix a drink, package a phone, or plug and unplug cables. The system records the human hand motion, tactile information, proximity, vision and position. That data can then be used to train OM-1 to control different robot bodies. Reward calls the idea: “One Model, One Data Interface, Any Body.” According to the company, a new task can be learned from less than 30 minutes of human demonstration data, without collecting additional robot data. The videos are worth watching. They show four robot arms packaging a phone, robots folding laundry and mixing cocktails, an arm unplugging an Ethernet cable, and early experiments with humanoids. Some of the longer tasks finish in under 30 seconds, in real time. There is also some history behind this. Reward AI builds on DexCap, work that came out of Stanford in 2024 around capturing human dexterous manipulation and transferring it to robots. @chenwang_j , C. Karen Liu and Li Fei-Fei were among the researchers involved. I think the bigger bet here is about the data layer. Robot hardware is changing quickly. If collecting training data means teleoperating every new robot body for thousands of hours, scaling gets expensive fast. Reward is betting that you can collect much of that knowledge from humans instead, then transfer it across different embodiments. That could become quite valuable if it works. For now, we only have the launch material. There is no paper or open model yet, and Reward has not published the kind of large-scale success-rate data needed to judge robustness. Still, this is one of the more interesting approaches I’ve seen to the robotics data problem recently. Source: rewardai.com/blog/OM-1/ —— Weekly robotics and AI insights. Subscribe free: 22astronauts.com