Have a sim vehicle in jolt-rust working with stacked hinges for wheel rotation and steering on two of four wheels, no suspension, visualized in @rerun.io:

Using @rerun.io , I established a baseline from the HoCAP dataset and conducted a qualitative comparison among the ground-truth calibrated cameras, Dust3r, and VGGT—all within rerun. The improvements are evident in both the camera parameters and the multi-view depth map/point cloud.

11/ Want early access or excited about building the future of Physical AI? Reach out here 👉 5li7zhj98k8.typeform.com/to/a5XDpBkZ or check out open roles: rerun.io/careers. Let's build!

10/ It comes with visualization built-in for fast data observability over both online and offline systems. The query engine enables you to combine vector search and full dataframe queries, over both raw logs and structured datasets, to support robotics-aware data science and dataset curation.

9/ We are now building a new database and cloud data platform for Physical AI. The database is built around the same data model as the open-source project.

8/ This data model is core to our popular open source framework for logging and visualizing multimodal data, which companies like Meta, Google, @hf.co's LeRobot, and Unitree Robotics have adopted it in their own open-source projects.

7/ We spent the first 2 years of the company iterating on a data model for Physical AI that works for both messy online logs and efficient columnar storage of offline pipeline data.

6/ Rerun is building a unified, multimodal data stack—one data model and platform supporting both online and offline workflows seamlessly, retaining semantic richness throughout.

5/ Until now, no single tool has spanned the entire stack from online data capture, to offline dataset management.

4/ Hand-written online code is being replaced with ML, requiring advanced offline pipelines to collect, manage, label, and curate massive datasets.

3/ Physical AI systems rely on two key components:

Online systems: Run live on robots, processing data and interacting with the real world in real-time;
Offline systems: Run in data centers, analyzing data and improving online systems through training and simulations.

2/ Physical AI—robotics, drones, autonomous vehicles—is rapidly evolving, powered by advances in machine learning. But today's data stacks aren't built for this new era.