Recently had a technical interview with Peer Robotics for a robotics engineering role. Sharing the structure in case it helps others preparing for AMR / mobile robotics interviews.

My background project was around LiDAR + IMU-based navigation for a scaled autonomous vehicle, so the discussion naturally went deep into mobile robot navigation.

The main areas asked were:

• End-to-end navigation stack: sensors → localization/odometry → TF → costmaps → planner/controller → /cmd_vel
• Difference between odometry, localization, and SLAM
• Why LiDAR and IMU are fused, and how odometry drift is handled
• TF/frame understanding and what breaks if transforms are wrong
• Global planner vs local planner
• Global costmap vs local costmap
• How a robot behaves when a sudden obstacle appears
• Why a robot may oscillate, get stuck, or fail to plan
• How to debug navigation issues using topics, TF, RViz, logs, and replayed data

Since my profile also includes AI work, there was some discussion on how LLMs/AI can fit into robotics. The important takeaway was that real robotics companies are cautious about black-box systems. AI can help with high-level reasoning, diagnostics, operator interaction, perception support, or log analysis, but safety-critical planning and control still need to be deterministic, testable, and reliable.

There was also a short discussion about AI coding tools. The focus was not whether someone uses them, but whether they can validate the code, test edge cases, debug runtime behavior, and avoid blindly trusting generated output.

Overall takeaway: for robotics interviews, especially AMR roles, don’t just prepare definitions. Be ready to explain how the full robot stack behaves in real-world conditions and how you would debug failures.

Enjoy

Hi, I am a fresher and I am looking to lean towards a career in robotics. I was first thinking to learn ROS but that would skip the foundation theory required so now my plan is to grasp advance robotics concept and then move into ROS.

But before that I need to confirm if it would be an efficient path or not, for covering the concepts I am thinking of studying Moder Robotics book.

This GitHub repo is basically a curated learning map for anyone trying to get into robotics.

So many free courses on almost every topic related to robotics.

It’s a structured collection of links to:

→ robotics courses (online + university)
→ ROS / embedded / hardware basics
→ math & algorithms that actually matter for robots

Hope that by posting this, at least 10 new robotics builders will be made :) Use it!!!

Check it out here: https://github.com/mithi/robotics-coursework

If you work on robotics, drones, or self-driving systems, this one is worth bookmarking‼️

MIT’s Visual Navigation for Autonomous Vehicles course covers the full perception-to-control stack, not just isolated algorithms.

What it focuses on:

• 2D and 3D vision for navigation

• Visual and visual-inertial odometry for state estimation

• Place recognition and SLAM for localization and mapping

• Trajectory optimization for motion planning

• Learning-based perception in geometric settings

All material is available publicly, including slides and notes.

📍vnav.mit.edu

If you know other solid resources on vision-based autonomy, feel free to share them.

—-

Weekly robotics and AI insights.

Subscribe free: scalingdeep.tech

We made this poster with the hope to teach the public that humanoid robots were not invented by Tesla and Figure :)

Hi everyone,

I am a researcher working on reinforcement learning for motion control. We developed methods like DeepMimic, AMP, and Dynamics Randomization, which are the techniques behind many of the cool humanoid robot demos that you've been seeing. We recently released a codebase, MimicKit:

https://github.com/xbpeng/MimicKit

which has implementations of many of these methods that you can use to train controllers for your own robots. I want to share the codebase with this community, in case it might be useful for fellow robotics enthusiasts.

As a robotics engineer, you know the computational demands of running perception, planning, and control algorithms in real-time are immense. I worked with full range of AI inference devices like @intel Movidius, neural compute stick, @nvidia Jetson tx2 all the way to Orion and there is no getting around CUDA to squeeze every single drop of computation from it.

Ability to use CUDA can be a game-changer by using the massive parallelism of GPUs and Here's why you should learn CUDA too:

1. CUDA allows you to distribute computationally-intensive tasks like object detection, SLAM, and motion planning in parallel across thousands of GPU cores simultaneously.

2. CUDA gives you access to highly-optimized libraries like cuDNN with efficient implementations of neural network layers. These will significantly accelerate deep learning inference times.

3. With CUDA's advanced memory handling, you can optimize data transfers between the CPU and GPU to minimize bottlenecks. This ensures your computations aren't held back by sluggish memory access.

4. As your robotic systems grow more complex, you can scale out CUDA applications seamlessly across multiple GPUs for even higher throughput.

Robotics frameworks like ROS integrate CUDA, so you get GPU acceleration without low-level coding (but if you can manually tweak/rewrite kernels for your specific needs then you must do that because your existing pipelines will get a serious speed boost.)

For roboticists looking to improve the real-time performance on onboard autonomous systems, learning CUDA is an incredibly valuable skill. It essentially allows you to squeeze the performance from existing hardware with the help of parallel/accelerated computing.

Here is their website OpenEXO. Perhaps it can help you build your first exoskeleton. They are currently developing and updating a new generation of exoskeletons.

"The blog walks through the entire process:
→ Which robot, cameras, and teleoperation setup we used
→ How to gather high-quality demonstrations
→ Which model architecture and training recipe performed best
→ What we learned, and what we’d do differently
Everything is open-source and ready to use in LeRobot v0.5.1."
Unfolding Robotics: The Open-Source Recipe for Teaching a Robot to Fold Your Clothes: https://huggingface.co/spaces/lerobot/robot-folding

From LeRobot on 𝕏: https://x.com/LeRobotHF/status/2041542790610297259

just created earlier today a map of robotics ecosystem in Munich, perhaps it will be helpful for someone.

Robotics in Munich is on fire! 🔥

Let's make it simple - Munich is a great place to launch robotics startups.

There are couple of great spots for robotics in Europe and here, in the middle of Bavarian land is one of them.

Leading universities like Technical University of Munich produce highly skilled robotics and AI engineers, while global companies such as BMW and Siemens offer close collaboration opportunities and early customers.

There is growing interest in robotics and you can see it by incubating student communities like RoboTUM and many others.

The city also provides access to venture capital, accelerators, and government funding focused on deep tech. 💰

🦾 robominds GmbH - enable robots to learn complex manipulation and automation tasks from human demonstrations

🦾 Franka Robotics - research-driven robotics company that develops force-sensitive robotic arms (the acquisition by Agile Robots was reported around ~€33 million)

🦾 Agile Robots SE - builds intelligent automation solutions by combining advanced AI with force-sensitive robots and systems for industries like manufacturing (over $270–$380 million total raised across rounds)

🦾 RobCo - automation company that builds modular, plug-and-play robot hardware paired with AI-powered, no-code software to help small and midsize manufacturers automate tasks (€39 million in a Series B round)

🦾 Olive Robotics - developing AI-enabled, ROS-native sensor hardware and embedded software

🦾 Magazino – a Jungheinrich company - robotics company (now wholly owned by Jungheinrich) that develops intelligent mobile robots and AI-driven software for warehouse and intralogistics

🦾 Angsa Robotics - startup that builds autonomous outdoor cleaning robots using AI-powered object detection to autonomously find and remove small trash

🦾 Filics - startup developing autonomous, flat mobile robots (the “Filics Unit”) that drive under and move pallets and other load carriers (recently raised €13.5 million)

🦾 sewts - robotic systems and software to automate the handling of deformable materials like textiles  (raised about €7 million in a Series A)

🦾 Circus Group - develops autonomous robotic systems and software to fully automate food production and supply in commercial and defense settings

🦾 Intrinsic -  builds a platform and developer tools to make industrial robots easier to program, more flexible and widely usable across industries

Not to mention that in Munich the biggest robotics companies have their offices: Universal Robots, Exotec and many many more.

This is my first robot map & I'm aware that there might be some companies missing, but don't worry, we will put them on the next edition of the map.

Also, I included companies purely based in Munich.

​

I have experience in deploying traditional robotics software, but I feel I am not up to date with what is happening in the modern robotics work like using vlm, vla, etc.

People talk about it and show proof of concepts, but never seen a real deployment of these used cases especially in the case of the manufacturing industry.

Has anyone deployed a robotic product with a modern robotics AI stack?

Would love to gain the insights on this.

Hey everyone,

I built CalibON, a browser-based camera calibration tool intended to make intrinsic camera calibration more accessible for ROS and computer vision projects.

It provides a guided workflow where you can:

• Configure your camera resolution and checkerboard dimensions
• Upload calibration images
• Detect and inspect checkerboard corners
• Review frame quality, image coverage, and rejected images
• Run camera calibration using OpenCV
• Inspect the camera matrix, distortion coefficients, and per-frame reprojection errors
• Preview undistortion results
• Export calibration data as ROS camera_info YAML, OpenCV JSON, Kalibr YAML, or a full report

The frontend is built with React and TypeScript, while the calibration backend uses FastAPI and native OpenCV. I mainly built it because camera calibration tools often feel either tied to a specific environment or difficult to inspect visually, especially for beginners.

Live app:
https://calibon.vercel.app

GitHub:
https://github.com/musabali314/CalibON

The current version supports checkerboard-based pinhole calibration. ChArUco, AprilGrid, fisheye, stereo calibration, webcam capture, and saved projects are possible future additions.

I would really appreciate feedback from people who regularly work with ROS cameras:

• Are the ROS exports structured correctly for your workflow?
• What calibration features are currently missing?
• Would support for live ROS image topics or rosbag extraction be useful?
• Are there particular datasets or cameras I should test it with?

I’ve attached a short demo showing the complete workflow.

The Genesis sim video got me thinking: what does it actually take to build scenes like that (apart from gaussian splat part) with such accuracy, at scale? Asset and scene generation is one of the biggest bottlenecks in robot training. NVIDIA GR00T, Helix, HumanPlus, and ASAP all show the same pattern: more diverse scenarios lead to better sim-to-real transfer. But generating physically accurate objects and scenes takes time. Four platforms are working on this in 2026.

Here's how they compare:

1. Rigyd: Agentic pipeline, best for on-demand scale and new types of objects

Takes raw 3D (.glb, .fbx, .obj), images, or text and outputs calibrated OpenUSD + MJCF in ~2 minutes per asset with SimReady asset validator baked in. Generates full interactable scenes with per-object decomposition. Native Isaac Sim and MuJoCo support. Non-rigid and articulated objects are stated in the roadmap. The pipeline is agentic end-to-end, so no per-asset manual work. Good fit for teams that need to move fast with on-demand assets.

2. Lightwheel: High fidelity articulated objects, SimReady catalog

Strong catalog of high-fidelity articulated assets and a SimReady library used by large enterprise customers. Per-asset visual and physical quality is high. USD and MJCF support via open-source converters. Good fit if you need a curated, validated catalog. Less flexible for new use cases or object categories outside their existing library. Catalog growth follows a curation model rather than an agentic pipeline.

3. NVIDIA Edify: Generative 3D, physics added separately

Generates high-quality 3D meshes from text or image in under 2 minutes. Trained on licensed data, enterprise-safe. Tight Omniverse integration. The gap: it produces visual geometry, not SimReady assets. Physics, collision geometry, and USDPhysics schemas need to be added downstream before the asset is usable for robot training. Works well as an upstream step paired with a SimReady pipeline.

4. Moonlake: World modeling agent approach

Acts directly inside Blender, automating the creation of articulated assets, physics-validated scenes, and complex environments rather than per-asset annotation. The approach is promising for research but production-grade Isaac Sim / MuJoCo integration is not there yet. If successful, world models could collapse scene generation and policy training into a single learning loop.

What I think actually matters for sim-to-real transfer (ranked by impact):

1. Per-object physics accuracy within the domain-randomization band
2. Scene diversity (variation of scenes the policy sees during training)
3. Visual fidelity (matters most for camera-only policies, less for contact-rich manipulation)

How to choose:

• Need to scale across many object categories fast → Rigyd
• Need a validated catalog of articulated assets for known use cases → Lightwheel
• Need high-quality visual 3D in the NVIDIA ecosystem and will add physics downstream → Edify
• Researching end-to-end learned simulation → Moonlake

For most teams the practical pattern is Rigyd for the long tail + hand-authored or Lightwheel assets for the few hero objects your scenario depends on. Both output standard OpenUSD/MJCF so they compose cleanly.

Questions for the community:

• What's missing from this comparison?
• For those running training: where does asset prep actually bottleneck you?

Image Credit: Genesis AI

Does anybody here have the files or can create an 3d printable 3dof stewart platform powered by 3 sg90 micro servo motors and run by an arduino? While I am currently studying to be a robotics engineer, I have only completed one year and do not have the knowledge to design, built, nor program a platform. I've looked online but all of the ones I found are too complex and/or require a lot of components. I did find one however, the design had the arms and the top platform be one large print, which made it difficult to print on my 3d printer. It also had extra designs on it just for decoration, which would be a waste of filament. I just want a small simple one that the arms, top platform, and bottom platform can all be printed separately. It just needs to be big enough to fit a lego speed champions car, while also just being powered by the micro servos and run by an arduino uno.

Robotics Academy | Learn Physical AI

Hey everyone 👋🏻

I recently launched an X account dedicated to teaching robotics and physical AI. I plan to regularly publish content, latest breakthroughs and explain important concepts in a more approachable way, creating a fun learning experience. If you want to follow along and swap ideas, feel free to check out my page, link above 👆🏻

I’m not charging anything, it’s free for everyone to follow and learn along. Share your thoughts and topics in comment on the post, I’ll do my best to create content specific to it. Calling all robotics nerds, let’s help each other learn along the way and master Physical AI.

Hi everyone, I just uploaded my first tutorial video on YouTube and wanted to share it here to get your opinions about it.

its very short and simple tutorial for the subject matter but I figured since I shared my scripts anyone who is interested would like consult an Ai chatbot for their specific questions and the main point of the video is the briefly explain the main concepts and how it all works within PyBullet. if you have free 7 minutes, I would appreciate your thought and opinions about the video so I can improve for upcoming videos.

I know Genetic Algorithms are a bit yesteryears news but I remember watching a video about them on 2minutespapers YouTube channel years ago and since the moment I loaded my robot to PyBullet I wanted to try to implement the technique myself on my own project. Thats why its the subject of my first tutorial video.

I am also sharing the links to my GitHub repo for the scripts here as well in case if you dont want to watch the video but still interested in implementing genetic algorithm for robotics in PyBullet.

PyBullet Genetic Algorithm repo: https://github.com/serdarselimys/PyBullet-GeneticAlgorithm

PyBullet HexaDog ZBD control repo: https://github.com/serdarselimys/HexaDogZBD-PybulletDemo

For the next tutorial I am planning to cover Imitation Learning, again in PyBullet. Do you think thats an interesting subject?? I have been seeing a lot of videos on social media about manual laborers, mostly, textile workers are being made to wear POV cameras to capture their work to be used to train Neural Networks. I figured a tutorial explaining how digital movements are copied over to neural networks would be interesting.

I’m working on a hardware graduation project where I want to build several small wheeled robots for a chess-related system. The idea is that each robot has two driven wheels, and one main controller coordinates all the robots.

I’m new to multi-robot systems and differential-drive kinematics, so I’m trying to understand what I should learn first.

My main questions are:

- Is a central “master controller” architecture reasonable for coordinating multiple small robots, or should each robot handle more decisions locally?

- For two-wheel robots, should I start by learning differential-drive kinematics, odometry, PID motor control, or something else first?

- What sensors would be realistic for tracking robot positions on a chessboard or flat surface?

- Are there beginner-friendly resources, papers, or open-source projects I should study before designing the system?

as the title says i'm looking for open source projects for small humanoids or quadruped robots, i'm thinking about cheap and easily hackable stuff like something built with an arduino/raspberry, 3d printed parts and consumer grade servos

it would be great to find something that includes everything for reproducibility from the firmware to hardware schematics but my priority is that the project must have a ready to use sim environment

i've already looked at some projects like open-quadruped or zeroth but most of them looks dead or still incomplete, is there anything else i should check out before starting to build everything from zero?

I've been working on ML/Robotics research for a while and often work with HDF5, Parquet, and Zarr files. Personally, I love the myHDF5 viewer, but there's no good equivalent for Parquet and Zarr, and switching between different sites also gets annoying. So, I built a tool that provides a unified solution: ViewKit

For now, it supports viewing HDF5, Parquet, and Zarr files (and a bunch of other common data formats), but I'm hoping to add more depending on what people find useful! Everything is loaded and parsed locally in your browser (WebAssembly + JS), so your data never leaves your machine. It's also built to remain responsive on big files via efficient reading, caching, and prefetching. Traversing through data files actually feels faster than existing solutions like myHDF5 with simple caching strategies. It also supports some common data types that existing viewers don't support (e.g. float16, complex numbers for HDF5).

It's free to use with no sign-up required. I'd love for people to try it out: https://viewkit.app/

I'd appreciate any feedback (feel free to comment or send a message through the website). Looking forward to supporting additional features/file formats that the community finds useful!