Blog | Robotiq

Getting a palletizing project done right has always depended on having the right information at the right time. Product specs, floor constraints, financial targets: when any of it is missing or wrong, the project pays for it later. IQ is the platform Robotiq built to change that. It captures the information behind a palletizing project, structures it, and generates a validated Workcell design bas…

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Most manufacturers who want to automate palletizing face the same problem. Getting a straight answer on whether it fits their operation, what it costs, and how long it takes has always required weeks of back-and-forth, engineering hours, and a site visit before anyone commits to anything. That is the problem Robotiq built IQ to solve.

Palletizing automation is one of the clearest wins in end-of-line operations. The ROI is real, the labor savings are immediate, and the technology is mature. Yet many manufacturers stall out, spending months on projects that should take weeks, or deploying systems that work in the demo but struggle on the production floor. The good news: most of these failures follow predictable patterns. Here ar…

To reach the level of robustness the Physical AI community aspires to, namely generalist policies deployable zero-shot on unfamiliar objects in unfamiliar settings, dataset sizes must grow by several orders of magnitude. To give a sense of scale, extending the logic to LLM-scale data volumes, on the order of 10¹², would require roughly 80 million robots operating continuously for three years . Th…

Vision-language-action models are the current state of the art in robotic manipulation. They still cannot pick up a potato chip without crushing it. That is the result published earlier this year by the team behind the Video Tactile Action Model (VTAM). On a potato chip pick-and-place task — a task that demands high-fidelity force awareness, where vision alone cannot distinguish a crushing grasp …

The best palletizing solution depends on your production volume, budget, available space, and need for flexibility . You can go with a fully engineered system, a cobot, or a plug-and-play setup. Each comes with tradeoffs. The key is picking what actually fits your floor, your throughput, and the return you expect. This quick guide compares the most common palletizing solutions so you can make an …

In 2016, I said something that went against where robotics was heading at the time: vision alone doesn’t work for grasping. Not “it needs improvement.” Not “the tech isn’t there yet.” It doesn’t fit the problem. Grasping is physical. Contact, force, friction. Vision can guide the approach. It can’t feel what happens next. Back then, we saw it in the lab. Tactile vibration data predicted grasp fai…

Medra Lab 001 is the largest autonomous AI-driven laboratory in the United States, operating continuously with robotics, AI, and adaptive grippers. Medra Lab 001 never sleeps. It reads the literature, designs experiments, runs them, analyses the results, and decides what to try next — continuously, without a human at the bench. Built across 38,000 square feet in under 90 days , it is already runn…

Pharmaceutical manufacturers are under pressure to increase output, maintain strict compliance, and protect their workforce, all within tightly controlled environments. Yet many facilities still rely on manual palletizing at the end of the line, where variability and risk are hardest to control. As a result, more pharmaceutical manufacturers are adopting robotic palletizing as a standard part of …

Physical AI is advancing quickly. AI models can now recognize objects, plan actions, and adapt to new tasks. But despite this progress, most systems still struggle to scale in real-world environments. Two core challenges explain why: Limited real-world dexterity High cost and complexity of deployment Until these are solved, Physical AI will remain difficult to scale beyond controlled applications.

Artificial intelligence has made impressive progress. Models can classify images, generate text, and even plan complex sequences of actions. But when you take AI out of the digital world and place it into a factory, a warehouse, or any physical environment, something breaks. The AI can decide. But it can’t reliably act. This is the gap that defines Physical AI —and it’s where most real-world robo…

TIDI Products, a global manufacturer of infection prevention and patient safety products, transformed its end-of-line operations by automating with Lean Palletizing. The result: measurable gains in productivity, safer working conditions, and more efficient use of labor. This case shows how robotic palletizing can directly improve manufacturing performance with clear, repeatable results.

Physical AI is evolving quickly. From imitation learning to foundation models, robotics teams are making real progress toward systems that can adapt, generalize, and improve over time. But there’s a gap. Many of these systems work well in controlled environments… yet struggle when faced with the variability of real production. If you’re a robotics OEM, product leader, or engineering team, you’ve …

Across Italy and the DACH region, food manufacturers are facing the same reality: 📈 Increasing production demand 👷 Labor shortages and rising costs ⚠️ End-of-line processes limiting throughput And more often than not, the bottleneck isn’t where you think. 👉 It’s palletizing .

Martin Ray Winery, a historic wine producer based in California, modernized its bottling operations by implementing a robotic palletizing solution . Results at a glance: Reduced manual palletizing labor Improved bottling line efficiency Increased operational reliability Expected ROI within 18–24 months This case highlights how collaborative robotic palletizers can solve labor shortages and produc…

Artificial intelligence has brought enormous excitement to robotics. Robots can now walk, navigate complex environments, and perform tasks that seemed impossible only a few years ago. But there is a major gap between robot demonstrations and real industrial deployment . A robot that works in a controlled research environment is very different from a robot that operates reliably on a production li…

Automation rarely starts with a full factory transformation. More often, it begins with a single line. One challenge, one opportunity, one team ready to try something different. What matters is what happens next.

Artificial intelligence has dramatically improved how robots perceive the world . Computer vision allows robots to detect objects, recognize patterns, and navigate complex environments. Cameras help robots identify parts on a conveyor, locate packages in a bin, and avoid obstacles in warehouses. But when a robot needs to pick up an object , vision alone is not enough. To manipulate objects reliab…

Coffee production is growing worldwide. From roasted beans to capsules and pods, manufacturers are increasing output to meet rising demand. Capsule formats such as Dolce Gusto® and other single-serve systems continue to expand in both retail and food service. But while packaging technology has evolved quickly, many coffee plants still rely on manual palletizing at the end of the line . That’s bec…