Introducing Instance: Evaluation for Robot Policies
Today Most Robotic (YC S26) is launching and showcasing their success detector for robot policies — the first piece of what will eventually be fully automated robot evaluation. You can try it now at demo.instancelabs.ai.
Instance launched its success detector for robot policies, the first piece of what the company intends to grow into fully automated robot evaluation. Teams describe a task and upload or record a rollout video, and the verifier judges whether the robot actually completed it.
The company was founded by Claire Mao and Lucy Cai, MIT-trained computer scientists who have been friends since middle school. Cai did humanoid robotics research in MIT's Learning and Intelligent Systems lab, wrote collision-avoidance software at SpaceX that handled the entire Starlink fleet, and built Amazon's first LLM-based unit test generation at AWS; Mao wrote planetary atmosphere simulation software at NASA JPL and did propulsion research at the MIT Media Lab. The idea came from Cai's robotics research, where she spent a year watching a humanoid attempt tasks and marking success by hand. The pair benchmarked their judge on more than 10,000 held-out, human-labeled episodes across 7 robot platforms and report it beats a frontier vision-language model on accuracy at a fraction of the latency, and they bought their own robot arm to train policies on.
