Common Mistakes in Robotics Problem-Solving

I've seen hundreds of automation projects go sideways. The culprits aren't always what you'd expect. Here are 5 counterintuitive mistakes that kill first-time automation projects: 1. Automating processes you don't fully understand True story: A client wanted to automate their packaging line. We discovered they had 7 undocumented steps operators were doing "by feel." You can't automate what you don't understand. Map EVERY step before you start. 2. Picking the "best" hardware for each component When starting Engineered Vision, I thought using the "perfect" hardware for each project was smart. Reality check: supporting 14 different PLCs and 8 robot brands created a maintenance nightmare. Standardization beats perfection. 3. Rushing past prototyping to "save money" Spending $15,000 testing a component that might fail seems expensive. Know what's more expensive? A $400,000 machine that doesn't work. Test your riskiest assumptions before full commitment. 4. Focusing on tech, not the problem "We need a 6-axis robot with machine vision!" Why? "Because it's cool." Wrong answer. Fall in love with the problem, not the solution. Often a SCARA or simple pick-and-place system outperforms fancier options. 5. Guess-and-check troubleshooting When something breaks, the worst approach is guessing what's wrong. Drive to the physics. Open the program, grab a voltmeter or oscilloscope and collect actual data. Understand exactly what's happening before making changes. The best automation projects start with clear process documentation, focus on solving actual problems, standardize where possible, test assumptions early, and troubleshoot methodically. What's the biggest mistake you've seen on an automation project?

I've seen million-dollar robots fail because of skipped testing protocols. I know what separates success from disaster. Here's the testing framework that saved my clients from costly failures: The robotics market is growing faster than safety standards can keep up. While manufacturers rush to market, there's no universal oversight body ensuring consistent standards. Most companies self-certify compliance. The results are showing up in workplaces everywhere. I've witnessed three critical failure patterns repeatedly: Programming errors slip through without third-party testing. Mechanical failures from rushed testing. When quarterly earnings pressure meets deployment deadlines, corners get cut. Sensor reliability issues in collaborative robots. The safety margins that look good on paper don't translate to factory floors. When something goes wrong, complex supply chains make it impossible to pinpoint responsibility. Manufacturers shift liability to customers through legal agreements. But proper robotics implementation looks completely different. Here's the testing framework we developed that changed everything: Pre-deployment: Run 100 hours minimum under peak load conditions. Document every anomaly. Integration testing: Verify all safety systems with deliberate failure scenarios. If the emergency stop hasn't been tested under full speed and load, it hasn't been tested. Human factors assessment: Watch actual operators interact with the system for full shifts. The surprises always come from real-world use. That's why we built RobotLAB around owning the implementation process. Every robot we deploy goes through comprehensive testing protocols. Having local teams nationwide means we're accountable for every deployment, not just the initial sale. This approach has helped hundreds of businesses implement robotics safely. If you're considering robotics for your business... Let's ensure you do it right from day one.

The most expensive mistake in robotics It is not a wiring fault or bad control loop. It’s skipping customer discovery. It is much more expensive than other industry or startup categories. I have seen many cool tech without actual customer use case. People build products that work. Yet, no one has talked to the person who’s supposed to use it. Robotics isn’t just about making it move once. It is about solving a repeatable problem that someone will pay for. That starts with discovery calls, site visits, and early pilot feedback before DVT or PVT ever begins. Skipping this step burns dollars and years. Because once you have locked in mechanical design, BOM, and firmware, every pivot costs ten times more. If you’re still in the duct-tape stage, validate before you optimize. The market won’t wait for perfect kinematics. Talk to 20 or more individuals that are your ICP (Ideal Customer Profile) before building the first prototype. Find them. Go to events where they appear.  Understand what they need, their current process, and even after building the first prototype, that is when you need to double-up on the interaction. Understanding what people will pay for will prevent the trap of building a cool technology nobody wants. And by the way, not all manual or dull and dirty, dangerous tasks you necessarily need a robot for. Automation can exist without robotics.

𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐬𝐭𝐚𝐫𝐭𝐮𝐩𝐬 𝐝𝐨𝐧’𝐭 𝐮𝐬𝐮𝐚𝐥𝐥𝐲 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐯𝐢𝐬𝐢𝐨𝐧 — 𝐭𝐡𝐞𝐲 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐩𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐰𝐨𝐫𝐥𝐝 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐛𝐞𝐧𝐝 𝐭𝐨 𝐢𝐭. I came across an interesting piece from The Robot Report about building and eventually closing a robotics company. It’s a good reminder of what actually breaks companies from the inside. These are very honest thoughts — I really appreciate people like Rui Xu and Benjamin Bolte who openly talk about failures and help others avoid making the same mistakes. ➡️ 𝗢𝘃𝗲𝗿-𝗿𝗲𝗹𝗶𝗮𝗻𝗰𝗲 𝗼𝗻 𝗔𝗜 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲𝘀 Teams lean too heavily on foundation models and “software-first” thinking, ignoring the constraints of hardware. Robotics is not just AI — it’s physics, integration, and reliability. ➡️ 𝗛𝗮𝗿𝗱𝘄𝗮𝗿𝗲 ≠ 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 You can’t patch a robot in production like you patch code. Every mistake compounds through design, manufacturing, and deployment. ➡️ 𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝗺-𝗱𝗿𝗶𝘃𝗲𝗻 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀 Aggressive roadmaps built on hope lead to shortcuts — and in robotics, shortcuts show up as failures in the field. ➡️ 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Getting one robot to work is easy. Scaling production, supply chains, and quality control is where most companies break. ➡️ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲𝘀 𝗸𝗶𝗹𝗹 𝗴𝗼𝗼𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 When leadership oversimplifies complexity (for fundraising or storytelling), teams lose alignment with reality. ➡️ 𝗧𝗵𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝘄𝗼𝗿𝗹𝗱 𝗮𝗹𝘄𝗮𝘆𝘀 𝘄𝗶𝗻𝘀 No pitch deck, demo, or hype cycle can override real-world constraints. 𝐒𝐨𝐦𝐞 𝐕𝐂 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬? I'm learning more and more spending time with robotics founders and you know, Robotics is not a “faster AI cycle” — it’s a fundamentally different game. 𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘄𝗶𝗻 𝗻𝗼𝘁 𝗼𝗻 𝗱𝗲𝗺𝗼𝘀, 𝗯𝘂𝘁 𝗼𝗻: • Systems engineering discipline • Manufacturing readiness • Real customer deployment loops • Conservative timelines with buffer for reality • and the TEAM! If anything, the bar for conviction should be higher than in pure software — not lower. The question I ask now isn’t anymore “𝒅𝒐𝒆𝒔 𝒊𝒕 𝒘𝒐𝒓𝒌?” It’s “𝒅𝒐𝒆𝒔 𝒊𝒕 𝒘𝒐𝒓𝒌 𝒓𝒆𝒍𝒊𝒂𝒃𝒍𝒚, 𝒓𝒆𝒑𝒆𝒂𝒕𝒆𝒅𝒍𝒚, 𝒂𝒏𝒅 𝒂𝒕 𝒔𝒄𝒂𝒍𝒆?” #Robotics #DeepTech #PhysicalAI #Startups #VentureCapital #Hardware #AI #Manufacturing #FounderLessons #TechInsights

I have witnessed a robotics CEO explain to investors why they need another $2M "The robot works perfectly," he said. "We just need to fix a few small issues." Those "small issues" were costing $100K per iteration. Here's what actually happened: Revision 1: "Just need stronger motors" - $50K Revision 8: "Robot chassis isn't rigid enough" - $400K Revision 15: "Heat dissipation problems, GPUs are failing" - $750K Bank account: Empty I've seen this movie enough times. Same plot, different actors. The real killer? Time. Software bug: Push fix at midnight, live by morning Hardware bug: 4 months of redesign, retooling, retesting It's like trying to edit a movie by reshooting every scene. Most robotics founders are hardware geniuses. But they're learning supply chain management with million-dollar mistakes. Here's the uncomfortable truth: You wouldn't perform surgery on yourself just because you're smart. Don't manufacture your first robot yourself just because you can design it. The best robotics teams focus on what makes their robot special - and partner with manufacturing experts for everything else. What's the most expensive "small fix" you've encountered?

𝗢𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘀𝗵𝗶𝗳𝘁𝘀 𝗜’𝘃𝗲 𝗺𝗮𝗱𝗲 𝗮𝘀 𝗮𝗻 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗼𝗿 is... Focus less on the robot and more on the problem. Don't get me wrong, we invest an immense amount of time and resources on training to be the most technically competent robot company we can be.  But I've learned to spend less time talking about the technology, robot or the company. 😆 In the early days, I focused on technical readiness—cycle time, fixturing, reach studies, simulation.  I felt like we had all the boxes checked technically, but for some reason the project died before it got approved. And those things still matter .... 𝘼𝙇𝙊𝙏 But they don’t matter if the project dies in approval. I’ve seen solid systems get sidelined because the people expected to run them never got a say in how they were built or why.  Technically it 𝘾𝙊𝙐𝙇𝘿 have worked, but it didn't go forward because the team wasn't ready enough to sell the project internally. No One has a Robot Problem!  The Robot is a tool to solve a problem.  Not the solution itself. Now, we approach projects differently. We ask WHY you think you need a robot. We ask HOW parts are run today. We ask WHO is responsible for what. We ask WHEN  you need it. We ask WHAT success looks like from multiple angles: 𝘵𝘰 𝘵𝘩𝘦 𝘱𝘦𝘰𝘱𝘭𝘦 𝘥𝘰𝘪𝘯𝘨 𝘵𝘩𝘦 𝘸𝘰𝘳𝘬, to management, to ownership, to the board, to other stakeholders. And sometimes the best solution is to pause the project until there’s alignment. It might feel slower in the beginning, but it saves months of headaches down the road. The robot is just the last piece of the puzzle. If the people around it aren’t part of the process, it won’t deliver. What are you seeing out there? Curious how others approach this on their shop floor. #automation  #manufacturing #leadership #roboticintegration #changemanagement #peoplefirst

Things to keep in mind in Robotics if you want to avoid failure If you work in robotics, there are a few hard lessons you need to keep in mind, especially if you want to avoid: 👉 demos that fail 👉 products that never make it to delivery Here are 3 common pitfalls I’ve seen more than once: 1) Be realistic with your roadmap You will run into problems. You will lose time solving things that looked trivial on paper. Sometimes it’s better to deliver less, but with 100% confidence, than to chase every business dream and miss your deadlines. 2) Hardware matters (a lot) If you rely on low-quality or poorly assembled hardware, issues are guaranteed to show up - usually at the worst possible moment. Murphy's Law is very real in robotics. 3) If your product is constantly running at 90% CPU, problems are just a matter of time. There are many more lessons like these, but these 3 are among the most common mistakes I see. Robotics is unforgiving - but with the right mindset, it’s also incredibly rewarding.

Most people think that the hardest problem in robotics is getting one robot to do every task. It’s not. Everyone who’s deployed a robot to a real customer knows this. The hardest problem is going the “last mile” from 90% reliability to 99.9%: rapidly adapting a robot to perform a specific task with extremely high performance. Why is this so hard? ⛔ Irreversible failures: In the physical world, critical mistakes like breaking an item or hurting a person are not acceptable. But it’s hard to avoid these failures without specifically designing the physical system to minimize these errors. 👀 Diagnosing mistakes: It’s not as easy as telling the robot what it should be doing in natural language. Observability systems are needed to ensure that when failures occur, they can be root caused. ⁉️ Lack of a fallback plan: When digital models don’t know something, they can search a knowledge base like the internet. When robots can’t figure out how to do something, there may be no automated fallback option. This is why it’s so critical to make seamless human intervention tools and systems. Read more here:  https://lnkd.in/gDYNXTEn