How to Program Robots for Complex Tasks

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

For developers working in robotics, Google has made its first Gemini robotics model, Robotics-ER 1.5, available to everyone in preview. This model is designed to be the high-level reasoning layer for an agent. It’s built to tackle complex, long-horizon tasks by breaking them down into an executable plan. Think "clean up the table" or "sort these objects into the correct bins according to local rules." A few of the technical capabilities developers can use: ✅  Tool Calling: It can natively call other functions, like Google Search (to find those "local rules") or, importantly, your own vision-language-action (VLA) models to execute the physical steps. ✅  Spatial & Temporal Reasoning: The model is tuned for fast, precise 2D spatial understanding (e.g., "point to all objects you can pick up") and can process video to understand the order of events. ✅  Flexible Thinking Budget: You can control the latency-vs-accuracy tradeoff. You can demand a fast, reactive response for simple tasks or let the model "think longer" to plan a more complex, multi-step action. ✅  Improved Safety Filters: Google has improved its ability to recognize and refuse plans that violate defined physical constraints (like a robot's payload capacity). This is available now in Google AI Studio and via the Gemini API. Getting started here: ✦ Paper: https://lnkd.in/eDqMHT2F ✦ Code: https://lnkd.in/eu-bgWky ✦ Docs: https://lnkd.in/eXNYRbrF

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

Trying to decide how to structure your AI agents for complex tasks? Not all agent setups are created equal. Whether you're building research assistants, automation workflows, or reasoning agents—your architecture matters. Here's a breakdown of 6 proven multi-agent structures and when to use them. 1. Simple Agent A single agent powered by an LLM calls tools to complete tasks. Easy to implement, but doesn’t scale well for complex jobs. 2. Network Multiple agents operate in a loop, sharing information directly. Great for peer collaboration, distributed reasoning, and exploration. 3. Supervisor One central agent delegates subtasks to others. Best for coordination, task management, and quality control. 4. Supervisor (As Tools) A supervisor agent is invoked like a tool by another agent. Enables modularity and expert-like behaviors embedded in other flows. 5. Hierarchical Agents are arranged in parent-child layers across levels. Ideal for structured workflows, decision trees, or step-by-step task pipelines. 6. Custom Mix and match multiple architectures to fit your domain. Perfect when flexibility and domain-specific logic are key. ✅ Use this cheat sheet to pick the right multi-agent architecture based on your use case, task complexity, and need for modularity or scalability.

Can a single neural network policy generalize over poses, objects, obstacles, backgrounds, scene arrangements, in-hand objects, and start/goal states? Introducing Neural MP: A generalist policy for solving motion planning tasks in the real world 🤖 Quickly and dynamically moving around and in-between obstacles (motion planning) is a crucial skill for robots to manipulate the world around us. Traditional methods (sampling, optimization or search) can be slow and/or require strong assumptions to deploy in the real world. Instead of solving each new motion planning problem from scratch, we distill knowledge across millions of problems into a generalist neural network policy.  Our Approach: 1) large-scale procedural scene generation 2) multi-modal sequence modeling 3) test-time optimization for safe deployment Data Generation involves: 1) Sampling programmatic assets (shelves, microwaves, cubbys, etc.) 2) Adding in realistic objects from Objaverse 3) Generating data at scale using a motion planner expert (AIT*) - 1M demos! We distill all of this data into a single, generalist policy Neural policies can hallucinate just like ChatGPT - this might not be safe to deploy! Our solution: Using the robot SDF, optimize for paths that have the least intersection of the robot with the scene. This technique improves deployment time success rate by 30-50%! Across 64 real-world motion planning problems, Neural MP drastically outperforms prior work, beating out SOTA sampling-based planners by 23%, trajectory optimizers by 17% and learning-based planners by 79%, achieving an overall success rate of 95.83% Neural MP extends directly to unstructured, in-the-wild scenes! From defrosting meat in the freezer and doing the dishes to tidying the cabinet and drying the plates, Neural MP does it all! Neural MP generalizes gracefully to OOD scenarios as well. The sword in the first video is double the size of any in-hand object in the training set! Meanwhile the model has never seen anything like the bookcase during training time, but it's still able to safely and accurately place books inside it. Since, we train a closed-loop policy, Neural MP can perform dynamic obstacle avoidance as well! First, Jim tries to attack the robot with a sword, but it has excellent dodging skills. Then, he adds obstacles dynamically while the robot moves and it’s still able to safely reach its goal. This work is the culmination of a year-long effort at Carnegie Mellon University with co-lead Jiahui(Jim) Yang as well as Russell Mendonca, Youssef Khaky, Russ Salakhutdinov, and Deepak Pathak The model and hardware deployment code is open-sourced and on Huggingface!  Run Neural MP on your robot today, check out the following: Web: https://lnkd.in/emGhSV8k Paper: https://lnkd.in/eGUmaXKh Code: https://lnkd.in/e6QehB7R News: https://lnkd.in/enFWRvft

𝗧𝗵𝗲 𝟳 𝗦𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 — 𝗙𝗿𝗼𝗺 𝗖𝘂𝗿𝗶𝗼𝘀𝗶𝘁𝘆 𝘁𝗼 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀 AI Agents are are becoming the backbone of intelligent automation in enterprises, startups, and personal workflows. But developing agentic systems isn’t a one-step task. It’s a structured evolution, and here's a clear roadmap to guide that journey: 𝗟𝗲𝘃𝗲𝗹 𝟭: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗪𝗵𝗮𝘁 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗜𝘀 Start with the basics: What makes an AI agent different from a chatbot or API? Stateless vs. stateful agents Understanding perception-action loops Single-agent vs. multi-agent logic  • Use cases: Guided chatbots, query bots, and task automation  • Tools: ChatGPT, Claude, Perplexity, ReAct, Hugging Face Spaces 𝗟𝗲𝘃𝗲𝗹 𝟮: 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗥𝗼𝗹𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 Shape how your agent responds, reasons, and behaves: Master zero-shot and few-shot prompts Design role-based agents Apply prompt chaining and task-specific templates  • Use cases: Research agents, content generators, email writers  • Tools: AIPRM, OpenAI Playground + PromptLayer, FlowGPT 𝗟𝗲𝘃𝗲𝗹 𝟯: 𝗔𝗱𝗱 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 Make agents smarter with memory: Integrate short-term and long-term memory RAG (Retrieval-Augmented Generation) Semantic chunking for better recall and relevance  • Use cases: Personal coaches, CRM bots, onboarding assistants  • Tools: LangChain Memory Modules, Weaviate, ChromaDB, Zep 𝗟𝗲𝘃𝗲𝗹 𝟰: 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲 & 𝗔𝗰𝘁𝗶𝗼𝗻 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 Agents that can do things, not just say things: Tool/function registration Web browsing, API calls, file execution Response augmentation and validation  • Use cases: Data scraping bots, email-sending agents, web-browsing AI  • Tools: OpenAI Functions, SerpAPI, ToolJunction, Plugin-enabled GPTs 𝗟𝗲𝘃𝗲𝗹 𝟱: 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗲𝗽 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 & 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 Now your agent plans, reflects, and self-corrects: Use TAP (task automation planning) Implement ReAct for reasoning + acting loops Handle complex task breakdown and self-evaluation  • Use cases: Business planners, customer support bots, QA systems  • Tools: AutoGen, LangGraph, MetaGPT, CrewAI, OpenAgents 𝗟𝗲𝘃𝗲𝗹 𝟲: 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Scale with teams of agents working in sync: Shared vs. local memory Role assignment and task division Feedback loops across agents  • Use cases: Sales AI squads, design + dev teams, collaborative review bots  • Tools: CrewAI, AutoGen (multi-threaded), AgentVerse, LangChain Executors 𝗟𝗲𝘃𝗲𝗹 𝟳: 𝗕𝘂𝗶𝗹𝗱 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘄𝗶𝘁𝗵 𝗥𝗲𝗮𝗹 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Now you're building true autonomous AI systems: Event-based triggers Lifecycle monitoring + fallback planning Real-world system integration  • Use cases: Back-office automation, end-to-end workflows, virtual AI workers  • Tools: BnB, Superagent, LangSmith, XAgents, TaskWeaver   

I've built 67+ AI agents in n8n. At first, I thought adding nodes and optimizing connections was what mattered. But I never really trusted them. Every output felt like a gamble. The bottleneck wasn't my architecture. It was my instructions. Avoid my mistakes and: 1. Separate static facts from inputs. Mixing them makes the agent guess context it should already know. → Example: Static = “Store opens at 9 AM.” Dynamic = “Order ID: 48281.” 2. Make the agent call out missing info. Guessing is the #1 source of silent failures. → Example: MISSING_FIELD: customer_email. 3. Force it to plan before acting. Step-planning stabilizes reasoning and reduces randomness. → Example: Plan internally. Output only the final result. 4. Give a fallback for impossible tasks. Without a fallback, the agent hallucinates a solution. → Example: ERROR_REASON: date_format_invalid. 5. Define “If X → Do Y” rules. Deterministic branching kills unpredictability. → Example: If date can’t be parsed → ask for a new one. 6. Allow creativity only where needed. Uncontrolled creativity = guaranteed hallucinations. → Example: Creative only in “Rewrite.” Everything else literal. 7. Limit the agent’s memory. Too much history makes the agent drift off-task. → Example: Use only the last 2 messages to determine intent. 8. Make it restate the task first. Repetition confirms the agent understood the request correctly. → Example: Task summary: extract the invoice number. 9. Validate inputs before generating outputs. Output built on bad inputs = guaranteed bad outputs. → Example: Invalid date: expected YYYY-MM-DD. 10. Require a termination signal. Your workflow needs a clear signal that the task is complete. → Example: End with “TERMINATE.” 11. Test your instructions with ugly inputs. If it only works on “happy path,” it’s not reliable - it’s lucky. → Example: Missing fields, malformed dates, weird formats. 12. Run a 10–20 sample eval before shipping. You can’t improve what you don’t measure. Vibes ≠ validation. → Example: Score each output: accuracy, format, tone, stability. 13. Iterate based on failures, not feelings. One word in your instructions can double your success rate. → Example: 2 outputs broke the format → tighten output rules. This is how you get from 30% to 80% success rate. Better instructions beat complex architecture. What's been your biggest challenge getting agents to behave consistently?

You're in a Senior Robotics interview at NVIDIA. The interviewer sets a trap: "We need a robot to open any drawer in any user's home. We cannot pre-train it on every possible handle shape. How do you build this?" 90% of candidates walk right into the "𝐃𝐚𝐭𝐚 𝐒𝐜𝐚𝐥𝐢𝐧𝐠" trap. They say: "We need more data. Let's scrape 10 million images of drawers or build a massive NVIDIA Omniverse simulation with procedurally generated handles. We'll train a massive end-to-end ResNet policy to map pixels directly to motor torques." 𝘛𝘩𝘪𝘴 𝘧𝘢𝘪𝘭𝘴 𝘪𝘯 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯. 𝘞𝘩𝘺? Because reality has an infinite long tail. The moment the robot sees a handle with a weird texture or a lighting condition your sim didn't catch, the end-to-end black box fails. They cannot brute-force "𝐓𝐡𝐞 𝐖𝐢𝐥𝐝." They aren't optimizing for 𝘮𝘦𝘮𝘰𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯. They are optimizing for 𝘤𝘰𝘮𝘱𝘰𝘴𝘢𝘣𝘪𝘭𝘪𝘵𝘺. Trying to teach a neural network to memorize the physics of every drawer in existence is a waste of compute. They don't need a bigger dataset, they need a smarter architecture that separates 𝘓𝘰𝘨𝘪𝘤 from 𝘗𝘦𝘳𝘤𝘦𝘱𝘵𝘪𝘰𝘯. ----- The Solution: You implement 𝐓𝐡𝐞 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐇𝐚𝐧𝐝𝐨𝐟𝐟. Instead of one giant model, you chain three specialized systems: 1️⃣ 𝐓𝐡𝐞 𝐏𝐥𝐚𝐧𝐧𝐞𝐫 (𝐋𝐋𝐌 𝐚𝐬 𝐂𝐨𝐝𝐞): You feed the instruction "Open the drawer" to an LLM. It doesn't output motor movements, it writes Python code. Output: handle_pos = detect(”drawer_handle”); robot.grasp(handle_pos) 2️⃣ 𝐓𝐡𝐞 𝐄𝐲𝐞 (𝐕𝐋𝐌): You use an Open Visual Language Model (like OWL-ViT or GPT-4V) to execute the detect() function. It looks at the chaotic real-world image and returns a bounding box for "drawer_handle." 3️⃣ 𝐓𝐡𝐞 𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐫: A traditional motion planner takes those coordinates and executes the kinematics. The LLM handles the logic (what to do). The VLM handles the variance (what it looks like). 𝐓𝐡𝐞 𝐚𝐧𝐬𝐰𝐞𝐫 𝐭𝐡𝐚𝐭 𝐠𝐞𝐭𝐬 𝐲𝐨𝐮 𝐡𝐢𝐫𝐞𝐝: "End-to-end training fails in the wild because you can't simulate entropy. I would use an LLM to generate policy code on the fly, grounded by a VLM for zero-shot object detection. We don't need the robot to memorize drawers, we need it to understand the concept of a handle." #ComputerVision #Robotics #EmbodiedAI #MachineLearning #VisionLanguageModels #LLMs #NVIDIA #AIInterviews #AutonomousSystems #AIEngineering

Enabling robots to understand and follow detailed instructions is both important and challenging. People want to give robots directions that are flexible, include specific landmarks, and check if the robot is doing things right. On the other hand, robots need to figure out exactly what people mean and how to act in the real world. This is where Language Instruction Grounding for Motion Planning (LIMP) comes in. Developed by a team of researchers at Brown University, LIMP helps robots follow complicated and open-ended instructions in real-world places, even if there aren't pre-made maps to guide them. LIMP creates a special representation of the instructions that shows if the robot is correctly understanding what the person wants it to do. This also helps the robot make sure its actions are accurate from the start. LIMP was tested with 150 instructions across five different real-world settings, showing that it works well in many new and unstructured places. In these tests, LIMP performed about the same as the top task planners and code-writing planners. However, when handling complex instructions that involve both time and space, LIMP succeeded 79% of the time, while the other planners only managed 38%. 📝 Research Paper: https://lnkd.in/exu3ctdT 📊 Project Page: https://rb.gy/94unhv 🎞️ Project Video: https://lnkd.in/eXpa3M-W #robotics #research

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