Rerun

@rerundotio • 7,623 subscribers

The data layer for physical AI. ⭐ GitHub https://t.co/yf1KZN7DBI 👾 Discord https://t.co/7PIlvsZO9n

Shorts

What does a real humanoid robotics dataset actually look like? Humanoid robots are expensive and complex, which makes their data unusually difficult to collect - and difficult for most people to get hands-on experience with. We previously shared a post on conversion code for the new Human in the Wild dataset by BitRobot 🦾, Hugging Face, and Unitree. Now we’re publishing the full HIW-500 data converted to RRD, so you can explore that data directly with Rerun. Watch the robot move. Inspect cameras and robot states together. Zoom in on a moment and query episodes through Rerun catalog! If you’re curious about a real-world humanoid dataset, HIW-500 is now a lot easier to explore. We share the conversion code along with our previous abc-130k dataset conversion example.

What does a real humanoid robotics dataset actually look like? Humanoid robots are expensive and complex, which makes their data unusually difficult to collect - and difficult for most people to get hands-on experience with. We previously shared a post on conversion code for the new Human in the Wild dataset by BitRobot 🦾, Hugging Face, and Unitree. Now we’re publishing the full HIW-500 data converted to RRD, so you can explore that data directly with Rerun. Watch the robot move. Inspect cameras and robot states together. Zoom in on a moment and query episodes through Rerun catalog! If you’re curious about a real-world humanoid dataset, HIW-500 is now a lot easier to explore. We share the conversion code along with our previous abc-130k dataset conversion example.

11,355 views

Rerun 0.29 is out 🚀 Big improvements for robotics workflows: better URDF handling, refreshed ROS 2 docs, and a new memory panel to debug your recordings. It also adds an experimental screenshotting API. Here's what's new 🧵

Rerun 0.29 is out 🚀 Big improvements for robotics workflows: better URDF handling, refreshed ROS 2 docs, and a new memory panel to debug your recordings. It also adds an experimental screenshotting API. Here's what's new 🧵

22,315 views

The upcoming Rerun 0.17 brings a huge increase in explicit control over your visualizations 🚀 In this video for example we dynamically change the visualizer that is applied on an entity from Mesh3D to Points3D and then edit the radius of the points. Because this mesh has vertex colors, the point cloud automatically becomes colored as well. All of this can be done either from UI or from code. We’ll release 0.17 next week. Stay tuned.

The upcoming Rerun 0.17 brings a huge increase in explicit control over your visualizations 🚀 In this video for example we dynamically change the visualizer that is applied on an entity from Mesh3D to Points3D and then edit the radius of the points. Because this mesh has vertex colors, the point cloud automatically becomes colored as well. All of this can be done either from UI or from code. We’ll release 0.17 next week. Stay tuned.

40,359 views

1/ We just raised $17M to build the multimodal data stack for Physical AI! 🚀 Lead: Point Nine With: Costanoa Ventures, Sunflower Capital, seedcamp Angels including: Guillermo Rauch, Eric Jang, Oliver Cameron, Wes McKinney , Nicolas Dessaigne , Arnav Bimbhet Thesis:

1/ We just raised $17M to build the multimodal data stack for Physical AI! 🚀 Lead: Point Nine With: Costanoa Ventures, Sunflower Capital, seedcamp Angels including: Guillermo Rauch, Eric Jang, Oliver Cameron, Wes McKinney , Nicolas Dessaigne , Arnav Bimbhet Thesis:

29,554 views

Did you know that NVIDIA's Isaac Lab comes with a Rerun viewer?

Did you know that NVIDIA's Isaac Lab comes with a Rerun viewer?

11,241 views

Introducing Rerun 0.13! With this release Rerun is (to our knowledge) the first publicly available multimodal visualizer with timeline scrolling that supports live visualization of time series in the kHz range. This is made possible by a 20-30x performance increase of time series plots.

Introducing Rerun 0.13! With this release Rerun is (to our knowledge) the first publicly available multimodal visualizer with timeline scrolling that supports live visualization of time series in the kHz range. This is made possible by a 20-30x performance increase of time series plots.

25,607 views

A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by Alexander Khazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.

A Rerun Viewer for the DROID Dataset! DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset is a robot manipulation dataset by Alexander Khazatsky et al. with 76k demonstration trajectories or 350h of interaction data, collected across 564 scenes and 86 tasks.

21,794 views

Rerun 0.20 is out! 🗺📍🎥 It adds early support for geospatial data with the new GeoPoints and GeoLineStrings archetypes and a map view. It also adds H.264 video support to the native viewer together with many performance and stability improvements for video in Rerun.

Rerun 0.20 is out! 🗺📍🎥 It adds early support for geospatial data with the new GeoPoints and GeoLineStrings archetypes and a map view. It also adds H.264 video support to the native viewer together with many performance and stability improvements for video in Rerun.

10,919 views

Videos

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Rerun 0.36 is out. Gaussian splats are now in the Viewer. Log them from the SDK or drop in a PLY file, with anisotropic scale, opacity, and view-dependent color. Also: recordings larger than your RAM in the browser, multi-sink streaming, better timeline nav. 🧵

Rerun 0.36 is out. Gaussian splats are now in the Viewer. Log them from the SDK or drop in a PLY file, with anisotropic scale, opacity, and view-dependent color. Also: recordings larger than your RAM in the browser, multi-sink streaming, better timeline nav. 🧵

25,225 views • 2 months ago

We're releasing XDOF s ABC-130k dataset converted to RRD on a Hugging Face bucket, along with the conversion code. ABC-130k is the largest open-source bi-manual teleoperation dataset published to date. With Rerun, you can view all the episodes, query for insights, and use them in training workflows. One interesting finding is that annotators use varying levels of granularity for subtask labeling. Take the chess task in this video, for example: One episode uses language like 'place the chess piece', while another uses a fine-grained description like 'place the queen/pawn at E8'. This distinction is clearly visualized in Rerun through the colors in the state view! Our repo also includes a "mistake" query example and other interesting observations about the dataset, complementing the official ABC-130k dataset card. Download a subset and explore it yourself!! What other open datasets would you like to see in RRD?

We're releasing XDOF s ABC-130k dataset converted to RRD on a Hugging Face bucket, along with the conversion code. ABC-130k is the largest open-source bi-manual teleoperation dataset published to date. With Rerun, you can view all the episodes, query for insights, and use them in training workflows. One interesting finding is that annotators use varying levels of granularity for subtask labeling. Take the chess task in this video, for example: One episode uses language like 'place the chess piece', while another uses a fine-grained description like 'place the queen/pawn at E8'. This distinction is clearly visualized in Rerun through the colors in the state view! Our repo also includes a "mistake" query example and other interesting observations about the dataset, complementing the official ABC-130k dataset card. Download a subset and explore it yourself!! What other open datasets would you like to see in RRD?

21,430 views • 1 month ago

🚀 Rerun 0.23 is out! The highlights: 🔸Backwards-compatible .rrd – open today’s recordings with tomorrow’s viewer. 🔸 Viewer callbacks – wire the viewer into your notebooks or web apps and react to selections and time-scrubs in real-time, e.g., to build annotation tools. 👇

🚀 Rerun 0.23 is out! The highlights: 🔸Backwards-compatible .rrd – open today’s recordings with tomorrow’s viewer. 🔸 Viewer callbacks – wire the viewer into your notebooks or web apps and react to selections and time-scrubs in real-time, e.g., to build annotation tools. 👇

55,438 views • 1 year ago

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

29,684 views • 8 months ago

Debug ROS 2 transforms in Rerun 💡 ROS 2’s tf2 gives robotics teams a shared language for tracking coordinate frames over time — transforming data between things like camera_link and base_link. Rerun supports the same idea with named transforms, letting you decouple spatial relationships from your entity hierarchy. This example skips the basics of transforms in Rerun (you can find that in our docs) and instead shows how to debug broken/missing transforms in practice, using the JKK dataset. Check out the how-to 👇

Debug ROS 2 transforms in Rerun 💡 ROS 2’s tf2 gives robotics teams a shared language for tracking coordinate frames over time — transforming data between things like camera_link and base_link. Rerun supports the same idea with named transforms, letting you decouple spatial relationships from your entity hierarchy. This example skips the basics of transforms in Rerun (you can find that in our docs) and instead shows how to debug broken/missing transforms in practice, using the JKK dataset. Check out the how-to 👇

15,982 views • 8 months ago

Rerun 0.21 is out! With it we’re introducing graphs in Rerun

Rerun 0.21 is out! With it we’re introducing graphs in Rerun

17,038 views • 1 year ago

Rerun 0.22 is out! 🔎🟡🔜🔵 The release brings long-requested entity filtering for finding data faster in the Viewer, significantly simplified APIs for partial & columnar updates, and many other enhancements. At Rerun we’re building the multimodal data stack for physical AI. Our open-source visualization tools help you model and visualize your data over time.

Rerun 0.22 is out! 🔎🟡🔜🔵 The release brings long-requested entity filtering for finding data faster in the Viewer, significantly simplified APIs for partial & columnar updates, and many other enhancements. At Rerun we’re building the multimodal data stack for physical AI. Our open-source visualization tools help you model and visualize your data over time.

15,092 views • 1 year ago

Check out the Neural Graph Mapping for Dense SLAM with Efficient Loop Closure paper by Leonard Bruns, Jun Zhang, and Patric Jensfelt, visualized with Rerun. “Existing neural field-based SLAM methods typically employ a single monolithic field as their scene representation. This prevents efficient incorporation of loop closure constraints and limits scalability. To address these shortcomings, we propose a neural mapping framework which anchors lightweight neural fields to the pose graph of a sparse visual SLAM system.”

Check out the Neural Graph Mapping for Dense SLAM with Efficient Loop Closure paper by Leonard Bruns, Jun Zhang, and Patric Jensfelt, visualized with Rerun. “Existing neural field-based SLAM methods typically employ a single monolithic field as their scene representation. This prevents efficient incorporation of loop closure constraints and limits scalability. To address these shortcomings, we propose a neural mapping framework which anchors lightweight neural fields to the pose graph of a sparse visual SLAM system.”

12,865 views • 2 years ago

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