AI is writing papers. Scientists are still loading centrifuges. We have built a perception, control, and manipulation stack that lets robots run experiments flexibly in scientific labs. We exist to expand the boundary of what is possible to automate today. Over the next week, we’ll share deeper dives into how our world models, mesh systems, motion planning, and LLM agents come together to make it possible. Centrifuges are everywhere in science - and a nightmare for automation. Here’s a quick preview of our system loading tubes into one.

Day 2 is down to the final sprint ⏳ After a few hours of sleep, teams are pushing their builds across the finish line. Who’s taking the win?

You do not need to be a roboticist to build with robots 🦾 Our hackathon participants are getting hands-on with the Zeon stack, using our software and robots to turn their ideas into scientific workflows. #AIforScience

The AI Science World Model Hackathon is officially underway at Zeon Systems 🤖🧪 Over the next two days, scientists and engineers will team up to build AI systems that can plan experiments, control robots and interact with real lab equipment. Let the building begin. #AIforScience

Simulated lab worlds straight from pixels. We scan the lab scene with depth cameras and reconstruct the environment. Each object already has a high-quality mesh in our object library from our mesh scanning system. A localization pipeline places those meshes into a shared coordinate frame, creating a digital version of the lab. This works well for science automation because the environment is constrained: the set of objects is limited, we have accurate models of them, and we don’t need to solve the full open-world perception problem. With this digital lab in place we can: • Drive safe motion planning around real geometry • Track state changes by re-localizing mesh parts (e.g. a lid opening) • Attach objects to the robot when picked so the planner accounts for them and avoids collisions built by @BastotdeHeijden @BlerimAbdullai @TahirMello

You need sub-millimeter precision to automate scientific labs. Calibration is crucial to achieving this. It resolves exactly where cameras and robot arms are with respect to the world and to each other. We have put a lot of work into robust camera and arm calibration so our system understands its environment precisely. Below we show a simple task: inserting a microcentrifuge tube into a tube rack. With proper calibration, inserting into these holes goes from impossible to achievable. - cc @BlerimAbdullai and @BastotdeHeijden for leading this work

Robots should be able to use any machine in a lab. To do that, robots need to reason about objects: their geometry, how they move, and how to avoid colliding with them. Our solution is meshes: 3D representations of real-world objects. We create these by leveraging depth information from cameras. With a mesh-based representation, we can explicitly model geometry, plan safe motions, and maintain precise alignment independent of lighting conditions or visual noise. We also learned the hard way that RGB data on it’s own (without depth) is not enough after several attempts to operate purely in pixel space. We built simple tools that let anyone: • Create meshes - scan objects with depth cameras or import existing models • Annotate meshes - mark joints, buttons, lids, and other moving parts • Build a shared mesh database - a growing library of annotated objects used by the system A new object can be scanned, annotated, and ready for the robot in no time. Below is an example of scanning and annotating a centrifuge mesh. cc @BastotdeHeijden @BlerimAbdullai for spearheading this work

Check out the end-to-end run here: youtube.com/watch?v=KogFyObc…