Robotics Engineering Technical Skills

As we transition from traditional task-based automation to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, understanding 𝘩𝘰𝘸 an agent cognitively processes its environment is no longer optional — it's strategic. This diagram distills the mental model that underpins every intelligent agent architecture — from LangGraph and CrewAI to RAG-based systems and autonomous multi-agent orchestration. The Workflow at a Glance 1. 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 – The agent observes its environment using sensors or inputs (text, APIs, context, tools). 2. 𝗕𝗿𝗮𝗶𝗻 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲) – It processes observations via a core LLM, enhanced with memory, planning, and retrieval components. 3. 𝗔𝗰𝘁𝗶𝗼𝗻 – It executes a task, invokes a tool, or responds — influencing the environment. 4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (Implicit or Explicit) – Feedback is integrated to improve future decisions.     This feedback loop mirrors principles from: • The 𝗢𝗢𝗗𝗔 𝗹𝗼𝗼𝗽 (Observe–Orient–Decide–Act) • 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 used in robotics and AI • 𝗚𝗼𝗮𝗹-𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 in agent frameworks Most AI applications today are still “reactive.” But agentic AI — autonomous systems that operate continuously and adaptively — requires: • A 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗼𝗼𝗽 for decision-making • Persistent 𝗺𝗲𝗺𝗼𝗿𝘆 and contextual awareness • Tool-use and reasoning across multiple steps • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 for dynamic goal completion • The ability to 𝗹𝗲𝗮𝗿𝗻 from experience and feedback    This model helps developers, researchers, and architects 𝗿𝗲𝗮𝘀𝗼𝗻 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗲𝗺𝗯𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — and where things tend to break. Whether you’re building agentic workflows, orchestrating LLM-powered systems, or designing AI-native applications — I hope this framework adds value to your thinking. Let’s elevate the conversation around how AI systems 𝘳𝘦𝘢𝘴𝘰𝘯. Curious to hear how you're modeling cognition in your systems.

Another robotics masterpiece from our friends from Disney Research! Recent progress in physics-based character control has improved learning from unstructured motion data, but it's still hard to create a single control policy that handles diverse, unseen motions and works on real robots. To solve this, the team at Disney proposes a new two-stage technique. In the first stage, an autoencoder is used to learn a latent space encoding from short motion clips. In the second stage, this encoding helps train a policy that maps kinematic input to dynamic output, ensuring accurate and adaptable movements. By keeping these stages separate, the method benefits from better motion encoding and avoids common issues like mode collapse. This technique has shown to be effective in simulations and has successfully brought dynamic motions to a real bipedal robot, marking an important step forward in robot control. You can find the full paper here: https://lnkd.in/d-kzexdJ What Markus Gross, Moritz Baecher and the rest of the gang are bringing to life is unbelievable!

Twelve years ago, I left a dream role building robotic dogs at Boston Dynamics to create a new class of robots that could solve construction’s biggest problems. I didn’t know what to expect as a founder in such a specialized space - how to raise funding, build a prototype, or assemble a team that could bring the vision to life. After eight years leading Canvas, my perspective and priorities have changed drastically. Here are the three biggest things I would tell any new founder in robotics: 1. Quantify Customer Needs Early Everything revolves around understanding and quantifying what your customer needs early and fast. Customers often can’t do this for you. Listen closely, study their workflow, define measurable outcomes, and share those metrics back, including price. Even if you’re off at first, it sparks the right conversation and ensures you know what to build. 2. Build for Reliability Hardware cycles are long, and you only get a few builds - pivoting isn't easy. That makes prioritization critical. Reliability is a critical and often overlooked customer need. It generally requires months of focus beyond the initial design and build to get right. Make it part of your plan from day one. 3. Set a few Clear Goals, then Hyperfocus on Them Your team is most motivated when they have clear, focused goals. Focus creates alignment and momentum. The team’s work is rewarded when customers love what they build — and that’s only possible if they understand what success looks like. Most importantly: hardware is hard. Robots are hard. That’s the reality and the opportunity. Embrace it, roll with it, and you might build something that changes everything.

Building a career in robotics and semi-autonomous vehicles in India requires a mix of technical education, practical experience, and industry networking. Here’s a structured approach: 1. Educational Pathway Undergraduate Degree, B.Tech/B.E. in: - Robotics Engineering - Mechanical Engineering - Electronics & Communication Engineering (ECE) - Computer Science with AI/ML specialization - Mechatronics Top Colleges in India: - IITs (Delhi, Bombay, Madras, Kanpur) - IIITs (Hyderabad, Bangalore) - NITs (Trichy, Surathkal, Warangal) - IISc Bangalore (for research-focused roles) - Private Institutes: BITS Pilani, VIT, SRM Postgraduate Specialization, M.Tech/M.S. in: - Robotics & Automation (IIT Delhi, IIT Kanpur, IISc) - AI & Autonomous Systems (IIIT Hyderabad, IIT Bombay) - Embedded Systems (NITs, DA-IICT) - Foreign Universities for Advanced Robotics: - Carnegie Mellon (USA) - Stanford, MIT (USA) - ETH Zurich (Europe) 2. Key Skills Required - Programming: Python, C++, ROS (Robot Operating System) - AI & ML: TensorFlow, OpenCV, PyTorch (for perception, decision-making) - Embedded Systems: Arduino, Raspberry Pi, Nvidia Jetson - Sensors & Perception: LiDAR, Radar, Computer Vision - Control Systems & Dynamics: Kinematics, PID controllers - Simulation Software: Gazebo, MATLAB, Simulink 3. Gaining Practical Experience Internships & Research Projects: - IITs and IISc offer robotics labs & research projects - Intern at companies like Tata Elxsi, Mahindra Electric, Ashok Leyland (autonomous vehicles) - DRDO, ISRO, and BARC have robotics-related projects Build Personal Projects: - Autonomous bots: Line-following robots, drone navigation - Self-driving car simulations: Use Udacity’s Self-Driving Car Nanodegree - Participate in hackathons: IIT RoboCon, Smart India Hackathon - Robotics Competitions - ABU Robocon India - Techfest IIT Bombay (Robotics Challenges) - Formula Student Autonomous (Self-driving race cars)

Debugging with AI is an A+ experience! Here is what I do, in very simple terms: 1. Ask the model to generate a few hypotheses Instead of asking directly for a fix, I ask the model to generate a few hypotheses of what could be happening and a proposed solution. Often, models attempt to fix the wrong thing or write excessive code that's unnecessary to solve the problem. Asking for hypotheses and potential solutions shows me what they are thinking and how they plan to solve it. 2. Ask for a reason, not a fix I'd rather ask, "What is causing this error?" than "What's the fix for this error." The former forces the model to provide a complete explanation I can review (and understand). The latter puts the model in "slop-code-generation" mode. 3. Always paste error logs I never say, "My code doesn't work." Instead, I drop the full traceback, test failures, or log output. The more, the merrier. Models are really good at parsing through all of this. 4. Explain what you've already tried This helps the model skip obvious dead ends. The more context you provide, the better the model suggestions will be. 5. Iterate, but be smart about it It's common to get stuck in a loop with a model that tries one solution, then another, and then back to the previous solution. The best way I've found to break out of these loops is to continually update the context and help the model with new clues. Another trick is to change models but share the entire context with the debugging session. Sometimes, one model can see what the other can't.

MIT Engineers teach household robots common sense 🤖🧹 Researchers at MIT have developed a way for robots to handle "surprises" in household tasks through learning from large language models. This approach allows robots to adjust to disruptions on their own, improving efficiency in completing tasks without human interventions. 𝗪𝗵𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄❓ 1. Robots can already mimic human actions, but they struggle with errors and disruptions. 2. The innovative approach allows robots to self-correct, reducing the need for human intervention. 3. This could significantly advance household robotics and bring robots with enhanced problem-solving abilities. 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸⚙️ - The team connected robot motion data with Large Language Models. - This allowed to break tasks into subtasks for easier adjustment and correction. - Then the algorithm converted training data into robust robot behavior, despite external perturbations. 👉🏻 𝗔𝗿𝗲 𝘆𝗼𝘂 𝘂𝘀𝗶𝗻𝗴 𝗿𝗼𝗯𝗼𝘁𝗶𝗰 𝗮𝗽𝗽𝗹𝗶𝗮𝗻𝗰𝗲𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗮𝘁 𝗵𝗼𝗺𝗲? Investment theme: Automation and Robotics - Source: MIT News / TechCrunch #investing #robotics #robots Thematic #investment #Litrendingtopics PS: Did you know that the world's first prototype of a ‘robotic’ vacuum cleaner was presented in 1997 by Electrolux? (Source: New Atlas)

As the creator of the first Saudi-made humanoid robots, “SARA” & “Mohamed” I believe the key to unlocking their full potential lies in designing them to reflect the culture, language, and customs of our region. Robots that speak our dialects, understand our traditions, and respect our values can truly resonate with people, driving adoption across industries in a way that feels natural and authentic.   This vision goes beyond functionality. It’s about creating robots that can connect on a human level; healthcare robots offering empathetic care in Arabic, educational robots engaging students with culturally relevant examples, or even customer service robots in retail and hospitality that mirror the warmth and respect our culture values.   To me, it’s not just about advancing technology; it’s about embedding our identity into it. By staying true to who we are, we can foster innovation while honouring the unique heritage of our region.   How else can we bring these cultural and linguistic nuances to life in robotics? I’d love to hear your thoughts. #SaudiTech #vision2030 #robotics #AI

These students were challenged to build a robot capable of scaling a vertical wall in record time, a task that mirrors real engineering problems faced by aerospace, manufacturing, and autonomous robotics teams worldwide. Will you be able to win? To succeed, each group had to master a full engineering cycle: 🔹 Mechanical design: calculating torque, motor ratios, surface grip, and center of gravity 🔹 Material selection: optimizing weight-to-strength ratios (aluminum, carbon fiber, 3D-printed composites) 🔹 Control algorithms: PID tuning, sensor feedback loops, and stability control 🔹 Energy efficiency: maximizing battery output and motor load under vertical stress 🔹 Failure analysis: testing, measuring, iterating, and rebuilding And this isn’t just academic. Challenges like this reflect real-world robotics breakthroughs: 📌 NASA’s Valkyrie robot uses similar balance and grip logic for climbing unstable surfaces in disaster response missions. 📌 Boston Dynamics spent over 10 years perfecting the control systems students experiment with on a smaller scale. 📌 Industrial robots used in warehouses face the same physics constraints — friction, payload, torque, and trajectory planning. 📌 Spacecraft design teams use identical modeling principles to ensure robots can maneuver on asteroids with extremely low gravity. And student innovation is accelerating fast: 🚀 University robotics teams report up to 40% faster prototype cycles thanks to rapid 3D printing. 🚀 High-school robotics programs now routinely use LIDAR, machine vision, and ROS, tools once limited to major research labs. 🚀 Over 90% of global robotics firms hire from hands-on competition pipelines like FIRST, VEX, and Eurobot. 🚀 The educational robotics market is growing 17% annually, driven by demand for engineers who can build, code, and troubleshoot under real conditions. Competitions like this create the mindset industry needs: not memorization, but building, breaking, fixing, optimizing — the same loop that drives innovation at the world’s leading tech companies. One student prototype at a time, the future of automation, AI, and robotics is already climbing upward. 🚀🤝 #Engineering #Robotics #STEM #Innovation #Education #AI #Automation #FutureOfWork #NextGenTech

Last week I spent 6 hours debugging with AI. Then I tried this approach and fixed it in 10 minutes The Dark Room Problem: AI is like a person trying to find an exit in complete darkness. Without visibility, it's just guessing at solutions. Each failed attempt teaches us nothing new. The solution? Strategic debug statements. Here's exactly how: 1. The Visibility Approach - Insert logging checkpoints throughout the code - Illuminate exactly where things go wrong - Transform random guesses into guided solutions 2. Two Ways to Implement: Method #1: The Automated Fix - Open your Cursor AI's .cursorrules file - Add: "ALWAYS insert debug statements if an error keeps recurring" - Let the AI automatically illuminate the path Method #2: The Manual Approach - Explicitly request debug statements from AI - Guide it to critical failure points - Maintain precise control over the debugging process Pro tip: Combine both methods for best results. Why use both?  Rules files lose effectiveness in longer conversations.  The manual approach gives you backup when that happens.  Double the visibility, double the success. Remember: You wouldn't search a dark room with your eyes closed. Don't let your AI debug that way either. — Enjoyed this? 2 quick things: - Follow along for more - Share with 2 teammates who need this P.S. The best insights go straight to your inbox (link in bio)

Humanoids may dominate the headlines—pouring coffee, doing backflips, and walking across factory floors—but they’re not where the real money is. The quiet winners? Specialized, task-focused robots. From Unbox Robotics boosting warehouse efficiency by 25% to Zipline delivering life-saving medical supplies, these single-task machines are quietly transforming industries. Okibo and Canvas are tackling drywall finishing, while Moxi roams hospital halls delivering supplies so nurses can spend more time with patients. Investors love them for one simple reason: ROI is clear and immediate. They’re cheaper to build, faster to deploy, and easier to justify on a balance sheet than flashy humanoids still stuck in pilot programs. The comparison is simple: forklifts changed the world, not Iron Man suits. The next wave of robotics won’t be about building machines that look like us—it will be about building machines that do the job better than us.