Real-world evaluations of AI and robots
Robocurve is a Public Benefit Corporation building open-source tools and independent benchmarks to help society understand the capabilities of AI and robots in the physical world.
Why it matters
Frontier labs are targeting general-purpose robotics by 2028, yet the field of robotics evals barely exists. Labs evaluate in-house, so no one actually knows how good anyone else is, or where the real frontier sits.
The arrival of general-purpose robots could drastically transform society. We want the public’s understanding of that future to be grounded in rigorous, verifiable data that is independent of any agenda.
Our work
We build open-source tools and benchmarks for evaluating robots.
Inspect Robots
Run any model on any embodiment on any benchmark, with full trace logs and live Rerun visualization.
If you know Inspect AI, this is that for robotics.
- Real-world first, with simulation support
- Supports running LLMs and coding agents such as Claude Fable 5 to control robots
- First-class integration with ROS, Isaac Lab, Robolab, Cap-X, and XPolicyLab's 40+ VLAs
- Open-source license (MIT)
World Evals
A catalog of benchmarks in the physical world, from making a sandwich to building a data center.
If you know Inspect Evals, this is that for robotics.
- Compatible with any form factor: arms, dexterous hands, humanoids, AMRs, quadrupeds, etc.
- Real-world first, with digital twins for simulation runs
- Benchmarks have well-specified distributions and reproducible setups
- Open-source license (MIT)
We welcome open-source contributions.
Contribute on GitHubAbout
Robocurve is a Public Benefit Corporation helping society understand the frontier of physical AI.
Our mission
Our mission is to independently evaluate and transparently report the real-world capabilities of robots and the AI models that control them.
Our team
Our team brings experience from Amazon Robotics, Amazon AGI Labs, AWS, the UK AI Security Institute, and Harvard’s Computational Robotics Lab, with published research at ICML, ACL, EMNLP, and ACM EC, and in IEEE Robotics and Automation Letters.