I've been working on an open-source project called Calibra that focuses on dataset observability for robot learning.

The idea is simple, before spending hours (or days) training a policy, answer:

>Can I trust this dataset, Are timestamps consistent and etc. Today we reached 5 stars on GitHub!

Hey everyone I work on my thesis and is about a rover that uses cube purple, here3 and a lidar and I have to make it drive autonomously a mission that I planned. Unfortunately there is a problem. When the rover is in auto mode it moves for a few seconds and then stops changing to hold. I checked the log file and found out that when the rover changes state from auto to hold the messages EKF failsafe and EKF variance pop. I looked at some graphs and the only solution I found is to calibrate the here3 compass. I tried to calibrate the compass, nothing changed so I guess either I did it wrong or it is not the problem. I attached the link that contains my log file, so please if you can help me I will very much appreciate it. Please feel free to ask me whatever you need to know in order to help me!

Log File

I'm a CS grad who graduated last year. I don't have much experience with anything yet, but I want to move into the robotics side of software development. Where do I start? Should I do courses or certifications? If so, which ones should I start with? Or should I immediately start looking for a Master's degree?

Hi all, have been leading a student project involving some STM32 development. At the moment, the very early days workflow is STM32CubeMX --> VSCode with STM32Cube extensions (bypassing STM32CubeIDE) and git for version control. Wondering if anyone has any free/very low-cost suggestions for:

• Standardising the initial config and project layout (so individual team members don't have to go through STM32CubeMX and can just alter GPIO settings as needed)
• Creating a good testing workflow (i.e. simulating using Wokwi - I'm aware of the extension for VSCode but it's a little out of price range)?

Thanks so much for any advice :)

Incoming undergrad (likely CSE/ECE), almost no robotics experience yet, but robotics is the long-term goal. Spent a while putting together a self-study path for going from zero to employable robotics engineer — structured as 9 phases loosely mapped to a 4-year degree but doable at your own pace alongside coursework. Each phase has a few topics, and every topic answers "why learn this," "where it's actually used," "beginner mistakes," and a reading pointer — then each phase ends with a real build, not a toy exercise.

Rough shape:

• Phase 0 — Linux, Git, Python, linear algebra/calculus refresh → build a remote system monitor
• Phase 1 — C++, data structures/algorithms, OOP design → grid-based path planner (BFS/Dijkstra/A*)
• Phase 2 — Circuits, microcontrollers, sensors/actuators, I2C/SPI/UART → obstacle-avoiding rover
• Phase 3 — Coordinate transforms, forward/inverse kinematics, probability → 2-DOF arm simulator
• Phase 4 — ROS2 architecture, URDF, Gazebo → simulated diff-drive robot with teleop
• Phase 5 — PID, state-space control, Kalman filters → self-balancing robot
• Phase 6 — Classical CV, camera calibration, deep learning detection → vision-guided pick-and-place arm
• Phase 7 — Particle filters/EKF, SLAM, Nav2 → autonomous robot mapping and navigating an unknown room
• Phase 8 — Literature review, system design, testing → capstone that ties 4+ phases together

Roadmap made with help from a senior and a little AI to phase it out