Foxglove

The Unitree Robotics LAFAN1 Retargeting Dataset visualized using Foxglove. 🤖 We had a little extra fun with this one—adding a feature to give the grid a fill color and adjusting another grid to shine down on the G1 dancing on the grass ☀️. Playfulness aside, this dataset employs some fascinating techniques. The retargeting process for this dataset uses numerical optimization techniques based on interaction mesh and inverse kinematics (IK). This approach ensures that retargeted motions adhere to end-effector pose constraints and joint position/velocity limits, effectively minimizing issues like foot slippage. However, this process focuses solely on kinematic constraints, excluding dynamic constraints and actuator limitations. As a result, while the dataset provides optimized motion trajectories, real robots may not execute these trajectories flawlessly due to unmodeled dynamic factors. But since Foxglove has a G1, we can always put it to the test, right? The LAFAN1 Retargeting Dataset is a valuable resource for researchers and developers working to improve humanoid robot motion. By providing retargeted motion data tailored to specific robot models, it helps advance the development of more natural and fluid robotic movements. Thanks for the dataset Unitree Robotics. Keep joy coming! Link to check out the dataset directly in Foxglove in the comments.

Welcome to Foxglove, Jaclyn Ting. We're very excited about everything you're bringing to Foxglove as the new Head of Finance.

In person time is the key to successful remote work. What? At Foxglove we’re fully remote, but hold week-long offsites twice a year. It’s a great opportunity to build friendships, hold ad-hoc discussions, and brainstorm future product direction. Feeling very energized after spending the week with this incredible team!

Evelyn "Fifi" Yeung, Reframe Systems' founding engineer, is passionate about building scalable and intelligent robotic systems to unlock climate-friendly housing as a basic human right. Fifi and some of the Reframe team were at #Actuate2024 last year. Check out what they had to say as we get ready to announce #Actuate2025!

📊 Unleash the power of reactive plots! Watch this demo on how to link the 3D panel with plot panels to visualize object trajectories. Learn how to filter data based on selected IDs and create interactive plots that respond to your selections!

The Unitree Robotics LAFAN1 Retargeting Dataset visualized using Foxglove. 🤖 We had a little extra fun with this one—adding a feature to give the grid a fill color and adjusting another grid to shine down on the G1 dancing on the grass ☀️. Playfulness aside, this dataset employs some fascinating techniques. The retargeting process for this dataset uses numerical optimization techniques based on interaction mesh and inverse kinematics (IK). This approach ensures that retargeted motions adhere to end-effector pose constraints and joint position/velocity limits, effectively minimizing issues like foot slippage. However, this process focuses solely on kinematic constraints, excluding dynamic constraints and actuator limitations. As a result, while the dataset provides optimized motion trajectories, real robots may not execute these trajectories flawlessly due to unmodeled dynamic factors. But since Foxglove has a G1, we can always put it to the test, right? The LAFAN1 Retargeting Dataset is a valuable resource for researchers and developers working to improve humanoid robot motion. By providing retargeted motion data tailored to specific robot models, it helps advance the development of more natural and fluid robotic movements. Thanks for the dataset Unitree Robotics. Keep joy coming! Link to check out the dataset directly in Foxglove in the comments.

💡 Spotlight: Técnico Solar Boat, a student-led team at Instituto Superior Técnico, is developing advanced solar and hydrogen-powered vessels to push the boundaries of sustainable maritime technology. Their latest autonomous prototype, São Pedro 01 (SP01), integrates stereo cameras, lidar, and AI-driven navigation. Using Foxglove, they visualize and debug real-time sensor data for improved autonomy. Their projects, including hydrofoil-equipped solar boats and hydrogen fuel cell vessels, demonstrate the future of clean maritime mobility. Check out the full spotlight article in the comments 👇

Adagy Robotics was in the house at #Actuate2024. Check out what Co-founder and CEO Rosalind Shinkle had to say about Adagy and Actuate. Are you ready for #Actuate2025?

In addition to the many 3D panel improvements in v2.22, we also made the Plot panel more intuitive and powerful. You can now set the x-axis values independently for each series in path-based (XY) plots. This means you can plot multiple series on the same plot, for example [x1, y1] and [x2, y2] where each of x1, y1, x2, and y2 are separate message fields. You can also now set custom axis labels for Plots and State Transitions panels to make your State Transitions and Plots easier to understand. Link to the changelog in the comments 👇

⛵️ The MIT Sea Grant AUV Laboratory's Prodromos marine perception dataset visualized using Foxglove. This multimodal dataset was designed to support research in autonomous surface vessel (ASV) perception and navigation. Data was collected using the R/V Philos, a 25-foot Boston Whaler equipped with a Velodyne VLP-16 LiDAR, 4G broadband radar, FLIR Blackfly RGB and ADK infrared cameras, and an AIS receiver. These sensors provide synchronized data streams essential for developing sensor fusion, object detection, and autonomous navigation algorithms. The dataset includes recordings from the Charles River and Boston Harbor, capturing diverse maritime scenarios such as sailboat and kayak traffic, commercial vessel operations in snow and rain, and bridge transits. A key feature is the integration of AIS data with radar imagery, enabling vessel identification and tracking in real-world conditions. AIS data was extracted via CANBOAT utilities from the vessel’s NMEA 2000 network, ensuring accurate timestamp alignment across all sensors. By providing high-resolution, real-world sensor data from a working research vessel, this dataset enables the development and validation of AI-driven perception and navigation models for autonomous maritime systems. Thanks for the very cool dataset, Captain Michael Sacarny, Michael DeFilippo, Dr. Supun Randeni, Dr. Milica Stojanovic, Filip Traasdahl Strømstad, and Raul Largaespada 🙏 Links to the project, dataset, and to view directly in Foxglove in the comments 👇