Practical Robot Programming Techniques

Build your first robot in simulation! 👾 📌 If you’re self-learning robotics, this is genuinely one of the better repos to save for later. NVIDIA Robotics released a "Getting Started with Isaac Sim" tutorial series covering everything from building your first robot to hardware-in-the-loop deployment. What's inside? → Building Your First Robot Explore the Isaac Sim interface, construct a simple robot model (chassis, wheels, joints), configure physics properties, implement control mechanisms using OmniGraph and ROS 2, integrate sensors (RGB cameras, 2D lidar), and stream sensor data to ROS 2 for real-time visualization in RViz. → Ingesting Robot Assets Import URDF files, prepare simulation environments, add sensors to existing robot models, and access pre-built robots to accelerate development. → Synthetic Data Generation Learn perception models for dynamic robotic tasks, understand synthetic data generation, apply domain randomization with Replicator, generate synthetic datasets, and fine-tune AI perception models with validation. → Software-in-the-Loop (SIL) Build intelligent robots, implement SIL workflows, use OmniGraph for robot control, master Isaac Sim Python scripting, deploy image segmentation with ROS 2 and Isaac ROS, and test with and without simulation. → Hardware-in-the-Loop (HIL) Understand HIL fundamentals, learn NVIDIA Jetson platform, set up the Jetson environment, and deploy Isaac ROS on Jetson hardware. The progression makes sense: start with basics (build a robot), add perception (sensors and data), generate training data (synthetic generation), develop software (SIL), then deploy to hardware (HIL). Each module builds on the previous one. For robotics teams, this is the path to faster iteration. Simulate first, validate in software-in-the-loop, generate synthetic training data at scale, then deploy to hardware with confidence. 🎓 If this helps at least one engineer to become more fluent in the world of robotics, means a lot to me! 🫶🏼 Here's the course (it's free): https://lnkd.in/dRYdkmdi ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

2–6 DOF Robotic Manipulators Trajectory Tracking using PID in MATLAB ➡ Simulation of 2-DOF to 6-DOF robotic manipulators ➡ Detailed modeling of serial manipulators including UR5 ➡ Forward & Inverse Kinematics implementation for all DOF systems ➡ PID-based joint control for smooth and stable motion ➡ Trajectory tracking: Circle, Rectangle, and Infinity (∞) paths ➡ Real-time 3D visualization and animation in MATLAB ➡ Modular and well-structured code for scalability and learning ✨ Why this matters: Trajectory tracking is a fundamental problem in robotics, where a manipulator must precisely follow a desired path while maintaining stability and accuracy. This becomes increasingly complex as the number of degrees of freedom increases due to nonlinear kinematics, joint coupling, and control challenges. This project demonstrates how classical control techniques like PID can be effectively applied to multi-DOF robotic systems to achieve smooth and reliable motion. By integrating kinematic modeling with control strategies, the system reflects real-world industrial applications where robotic arms are required to perform precise tasks such as assembly, welding, and pick-and-place operations. 📊 Key Highlights: ✔ Complete kinematic modeling (FK & IK) for 2–6 DOF manipulators ✔ PID-based trajectory tracking for accurate motion control ✔ Implementation of multiple trajectories (circle, rectangle, infinity) ✔ Real-time simulation and visualization in MATLAB ✔ Clean and reusable code structure for educational use ✔ Industrial-level modeling with UR5 6-DOF manipulator 💡 Future Potential: This framework can be extended to: ➡ Advanced control (Adaptive, MPC, Fuzzy, AI-based control) ➡ Obstacle avoidance and path planning ➡ Integration with ROS 2 for real robot deployment ➡ Dynamic modeling and torque control ➡ Digital twin and industrial automation systems 🔗 For students, engineers & robotics enthusiasts: This project provides a complete hands-on approach to understanding robotic manipulators, control systems, and trajectory planning. It is ideal for learning how robotic arms achieve precise motion in real-world applications. 🔁 Repost to support robotics innovation & engineering learning! #Robotics #MATLAB #PIDControl #RobotManipulators #UR5 #ControlSystems #Automation #Mechatronics #EngineeringProjects #Simulation #STEM #EngineeringEducation

⭐️ We're releasing a comprehensive, hands-on recipe for teaching robots to fold clothes 🤟 … a 25 min read with a full breakdown of modern end-to-end robot learning from hardware to training to evaluation, all open-sourced with LeRobot and Hugging Face 🤗. → Built from 131 hours of teleoperation data, 5k+ GPU hours, 8 robot setups, and a set of practical findings we didn’t expect 👀 We trained language-conditioned vision-action policies for bimanual cloth folding, reaching 90% success on arbitrary t-shirts on real hardware. But the most interesting result wasn’t the model. With architecture and training held fixed, performance moved from 40% → 90% almost entirely by changing the data: – making demonstrations more consistent (same strategy each time) – selecting higher-quality trajectories instead of using everything – giving the model a notion of “progress” through the task (SARM) – adding examples of how to recover from mistakes (Dagger-style) This suggests a useful lens: For long-horizon, contact-rich tasks, we are not yet model-limited. Performance depends heavily on how we structure and supervise interaction data over time. Concretely: – consistency helps more than showing many different ways of doing the task – learning which parts of a trajectory matter is more important than treating every step equally – teaching the model how to recover from failure is as important as showing successful executions We wrote this as a detailed, reproducible system for others to build on. hope it’s useful if you’re working on real-world robot learning. Blog: https://lnkd.in/dW_8JKD9

🤖 *From Woodpecker to Working Robot: My 3-Week Journey Training ACT on SO-101* 🤖 I trained my SO-101 to pick and place blocks, using Action Chunking Transformer (ACT), an imitation learning model. After 3 weeks, a broken motor, and countless mistakes, I got 90% in-distribution success! (75% out-of-distribution) What makes this different from other ACT tutorials? I'm sharing ALL the failures: - Try 1: Trained a "woodpecker" that just pecked the table repeatedly - The great motor breakdown (RIP to my gripper servo) - Camera disconnection nightmares - Why looking over your shoulder while teleoperating is cheating - Data coverage gaps that killed generalization But also the solutions: ✅ Proper hardware standardization ✅ USB udev rules to fix camera disconnections ✅ Stratified sampling for better data diversity ✅ Building a repeatable eval pipeline with progress scoring ✅ 125 episodes with orientation variations = actual generalization Key lesson: Consistent setup + data diversity + proper debugging tools = magic! This was messy, frustrating, but absolutely worth it! If you're starting with SO-101, Hugging Face LeRobot, or ACT, I hope my journey will save you some time. Full blog post (with videos, code, and way more detail): [Link in comments] What's been your biggest lesson working with real-world robot learning? #Robotics #MachineLearning #AI #HuggingFace #LeRobot #ImitationLearning #OpenSource #ActionChunkingTransformer

🚀 Getting Started with Real-World Robots with LeRobot from Hugging Face (Top 60+ open-source robotics projects for beginners- Video: https://lnkd.in/eTYHRfYh Blog article with presentation: https://lnkd.in/e2E5_Jje) 🛠️ 1. Order and Assemble Your Koch v1.1 🌐 Follow instructions on the Koch v1.1 Github page for detailed assembly guidance. 📺 Visual walkthrough: Assembly video provides step-by-step visual instructions. ⚙️ 2. Configure Motors, Calibrate Arms, Teleoperate a. Control Motors with DynamixelMotorsBus 🔄 Configure Motors: Assign unique indices to each motor for proper communication. b. Teleoperate with KochRobot 🏗 Instantiate KochRobot: Create a robot instance using pre-configured arms. 🧮 Calibrate Robot: Align the leader and follower arms for synchronized movements. 🕹 Teleoperate: Manually control the robot by moving the leader arm, which directs the follower arm. c. Add Cameras with OpenCVCamera 🔍 Find Camera Indices: Detect available cameras and assign indices for identification. 📸 Instantiate Camera: Connect and initialize the cameras using OpenCV. 🎥 Add Cameras to Robot: Integrate camera feeds into the robot system for real-time visual feedback. d. Use koch.yaml and Teleoperate Function ▶️ Run Teleoperate Script: Utilize the YAML configuration file to automate teleoperation setup and execution. 🎥 3. Record Your Dataset and Visualize It a. Use koch.yaml and Record Function 📹 Record Data: Capture state and action data during teleoperation for later use in training. b. Tips for Recording Dataset 🏁 Start with simple tasks (e.g., grasping objects) to build a foundational dataset. 🎥 Record multiple episodes for consistency and better training data. 🔄 Gradually introduce variations to improve the robustness of your model. c. Visualize All Episodes 👀 Run Visualization Script: Review recorded episodes using visualization tools for analysis and debugging. d. Replay Episode 🕹 Run Replay Script: Test the repeatability of recorded episodes by replaying actions on the robot. 🧑🏫 4. Train a Policy on Your Data a. Use the Train Script 🧠 Run Train Script: Train a neural network policy using the recorded dataset for autonomous robot control. b. (Optional) Upload Policy Checkpoints ☁️ Upload Latest Checkpoint: Share your trained model by uploading checkpoints to the cloud. 🧪 5. Evaluate Your Policy a. Use koch.yaml and Record Function 🧪 Run Evaluation Script: Perform evaluation runs using the trained policy and record the results. b. Visualize Evaluation Afterwards 👀 Visualize Evaluation: Analyze the performance of your policy through visualized evaluation data. Tutorial link: https://lnkd.in/eFCmE2ut Source: https://lnkd.in/ePQJbaWv

Hard but achievable roadmap to go from beginner roboticist to a Robo Renegade Engineer with ROS (Robotics Operating System). (Assuming you have coding proficiency). 1. Get a fully working example simulation with code on your computer and start to playing with it. You can try to: - Experiment with 'ros2 node' and 'ros2 topic' commands, especially 'ros2 topic echo' to see how information is flowing around the system. - Experiment with the robots sensor settings, see how the algorithms work when their sensor range is increased or reduced. - Experiment with cost-map settings (i.e. inflation radius) to see how that effects the path planner. - Experiment with modifying launch files for different tasks, such as mapping for saving a map, and navigation off a saved map. - Experiment with different plugins for Nav2 behavior trees. 2. Try solving a problem with code. Begin with the ROS listener and publisher examples, but quickly move on to: - Create a waypoint controller. A node that monitors the robots progress and sends it on a looping patrol. This will help you to understand ROS topics and actions. - Create a Web GUI to drive your robot, complete with a map. This will expose you to the different data types and requirements, and a new programming language, JavaScript. 3. Modify or create a new robot Find a robot you want to model and simulate it by writing a URDF and using basic CAD. This will expose you to the how gazebo plugins are used and how ros2_control is used. It should also be a lot of fun. 4. Modify Gazebo to create a plugin to simulate the scenario. You could simulate a mining truck that gets given a load when it reaches a certain waypoint, and the load is removed when it reaches another waypoint. This will require you to understand the ROS plugin system and extend Gazebo classes. 5. Create your own Gazebo controller with ros2_control This will expose you to how things work inside ROS with C++ and give you an understanding of the ros2 control system. This will be particularly helpful when its time to build a real robot, as you won't be afraid of writing a ROS control driver for your servo's or whatever else you need. 6. Build a real robot. Take all your skills so far and deploy it to a real robot.

Most RL tutorials stop at simulation or show impressive hardware results without explaining the engineering process that made them work. This guide bridges the gap to real hardware with a complete working system – training code, hardware deployment, 3D models, trained checkpoints – and comprehensive documentation of the engineering methodology that made it work. You get the reward design process, sensor characterization approach, debugging frameworks, and decision-making that got RL working on a real robot. What could take you months of trial and error is compressed into a proven methodology you can follow in days. What You’ll Be Able to Do - Build accurate MuJoCo models that enable hardware transfer - Train RL policies that work on real robots, not just simulation - Systematically debug sim-to-real failures - Apply this methodology to more complex robots (humanoids, quadrupeds) https://lnkd.in/gWRmDxDs

Inverse Kinematics - Part VI: Implementation on the real robot After validating the IK functions through simulation, it’s time to finally implement them on the real robot! To do this, we write an inverse kinematics function in C++. The function takes a float array with the desired pose (X, Y, Z, and R) as argument, and returns a struct. The struct has two variables: the first is a boolean indicating if the desired pose is reachable, and the second is a float array with the four resulting joint angles if that’s the case. In the video I show the robot being moved linearly with the teach pendant in each coordinate, and the cartesian positions being plotted. The program flow works like this in jogging mode: - The robot’s microcontroller communicates with the teach pendant’s via I2C. It requests which button the user is pressing. - Based on the button being pressed, the controller calls the trajectory generating function to move that specific coordinate. - The trajectory function accelerates until a constant velocity is reached. When the user releases the button, it decelerates until stopping. - At every iteration, the trajectory function calls the inverse kinematics function, passing the desired cartesian position. - In case the position is reachable, it calls the motor command function, passing the resulting joint angles. - The motor function moves each joint to the desired new value, and both the new cartesian and joint positions are updated on the program. This happens at a rate of 100Hz (every 10ms). At a lower rate (10Hz), the robot’s controller sends the new position to the teach pendant for it to be updated on the screen! Moving a robot linearly may seem like something simple, but it is the culmination of different robotics topics being implemented, plus the programming making it all work together in synergy! As always, feel free to leave any questions in the comments. On next posts I’ll share the theory and implementation of trajectory generating functions, both in joint and cartesian space. #robotics #kinematics #inversekinematics #roboticar #robot