Rodney Brooks Perspective on AI and Robotics

Rodney Brooks, co-founder of Rethink Robotics, iRobot and Robust.ai and the Panasonic Professor of Robotics Emeritus at MIT says many “humans tend to overestimate [generative AI]’s capabilities.” “When a human sees an #AI system perform a task, they immediately generalize it to things that are similar and make an estimate of the competence of the AI system; not just the performance on that, but the competence around that. And they’re usually very over-optimistic, and that’s because they use a model of a person’s performance on a task.” He says people see it as so capable they even want to use it for applications that don’t make sense such as in his latest company, Robust.ai. “Someone suggested to him recently that it would be cool and efficient to tell his warehouse #robots where to go by building an LLM for his system. In his estimation, however, this is not a reasonable use case for generative AI and would actually slow things down. It’s instead much simpler to connect the robots to a stream of data coming from the warehouse management software.” “When you have 10,000 orders that just came in that you have to ship in two hours, you have to optimize for that. Language is not gonna help; it’s just going to slow things down,” he said. “We have massive data processing and massive AI #optimization techniques and planning. And that’s how we get the orders completed fast.” Another lesson is that you can’t try to do too much. “We need to automate in places where things have already been cleaned up. So the example of my company is we’re doing pretty well in warehouses, and warehouses are actually pretty constrained. The lighting doesn’t change with those big buildings. There’s not stuff lying around on the floor because the people pushing carts would run into that. There’s no floating plastic bags going around. And largely it’s not in the interest of the people who work there to be malicious to the robot." Even if sufficient cleanup was done, "Brooks says we have to accept that there are always going to be hard-to-solve outlier cases when it comes to AI, that could take decades to solve. Without carefully boxing in how an AI system is deployed, there is always a long tail of special cases that take decades to discover and fix.” Brooks also says "that there’s this mistaken belief, mostly thanks to Moore’s law, that there will always be exponential growth when it comes to technology — the idea that if ChatGPT 4 is this good, imagine what ChatGPT 5, 6 and 7 will be like.” “People say, ‘Oh, the large language models are gonna make robots be able to do things they couldn’t do. That’s not where the problem is. The problem with being able to do stuff is about control theory and all sorts of other hardcore math optimization.” #technology #innovation #startups #hype https://lnkd.in/gjPZ5sbH

Rodney Brooks pours cold water on humanoids but… Renowned roboticist Rodney Brooks warns that today’s humanoid boom may be a bubble destined to burst. His concerns? Hands are still far from human dexterity, safety risks scale with size, and energy demands make humanoids costly and fragile. But… 🔹 The enormous R&D pouring into humanoids—actuators, sensors, control systems, AI—won’t vanish if the bubble pops. 🔹 These advances can flow into other robotics forms: wheeled robots, mobile manipulators, surgical robots, warehouse automation, and more. 🔹 Even if humanoids don’t win in the short term, the research 𝐦̶𝐚̶𝐲̶ will accelerate the broader robotics field. Sometimes the biggest breakthroughs happen when ambitious ideas don’t pan out exactly as planned—but leave behind a trail of technology that reshapes what does work. 🔧 ATI Industrial Automation and Celera Motion, A Novanta Company support physical AI in any form. #robotics

𝐖𝐡𝐲 𝐇𝐮𝐦𝐚𝐧𝐨𝐢𝐝 𝐑𝐨𝐛𝐨𝐭𝐬 𝐒𝐭𝐢𝐥𝐥 𝐂𝐚𝐧’𝐭 𝐌𝐚𝐬𝐭𝐞𝐫 𝐇𝐮𝐦𝐚𝐧 𝐃𝐞𝐱𝐭𝐞𝐫𝐢𝐭𝐲 As humanoid robots gain attention—and billions in investment—veteran roboticist Rodney Brooks offers a sobering reminder: true human dexterity remains far beyond the reach of today’s models. Why Today’s Humanoids Won’t Learn Dexterity, Brooks explores why brute-force machine learning and video-based imitation are not enough to replicate the richness of human touch and control. 👉𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐌𝐢𝐫𝐚𝐠𝐞 ▪Companies like Tesla and Figure are betting that training humanoids purely on videos of human motion will teach dexterity. ▪Brooks argues this belief is misguided because visual data alone cannot capture the tactile richness that defines human manipulation. 👉𝐓𝐡𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐈𝐥𝐥𝐮𝐬𝐢𝐨𝐧 ▪End-to-end learning worked for speech and vision only because those systems had decades of human-engineered preprocessing—phones, cameras, and convolutional neural networks—all optimized to extract structured data. Robotics has no such standardized “touch pipeline”. 👉𝐓𝐨𝐮𝐜𝐡: 𝐓𝐡𝐞 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐌𝐨𝐝𝐚𝐥𝐢𝐭𝐲 ▪Human dexterity stems from a deeply complex sensory web. The human hand has roughly 17,000 mechanoreceptors, including 1,000 at each fingertip, enabling nuanced grip, texture sensing, and feedback. Robot hands have nothing comparable. 👉𝐓𝐡𝐞 “𝐑𝐢𝐠𝐡𝐭 𝐓𝐡𝐢𝐧𝐠” 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 ▪Reinforcement learning trains robots to map sensor inputs directly to actions—but humans don’t operate that way. We plan, adapt, and modulate actions dynamically. ▪Dexterity involves intermediate “plans” and context-driven decisions that AI models can’t yet emulate. 👉𝐓𝐡𝐞 𝐓𝐡𝐢𝐫𝐝 𝐋𝐚𝐰 𝐨𝐟 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 ▪Brooks reaffirms his “third law”: We are more than ten years away from the first profitable deployment of humanoid robots, even with minimal dexterity. 👉𝐀 𝐂𝐚𝐥𝐥 𝐟𝐨𝐫 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 ▪If even 20% of current humanoid funding went toward foundational tactile and sensory research, Brooks argues, we’d progress faster toward real-world robotic usefulness. Brooks’ essay is a critical reality check amid the humanoid hype cycle. In the race to automate human motion, the robotics world risks forgetting what truly makes us dexterous—our sense of touch, feedback, and embodied intelligence. Progress will come, but not from brute data or imitation alone. True dexterity, it seems, will require rethinking what "learning" means for machines. 𝐒𝐨𝐮𝐫𝐜𝐞/𝐂𝐫𝐞𝐝𝐢𝐭: https://lnkd.in/g2S_jye4 #AI #AgenticAI #DigitalTransformation #GenerativeAI #GenAI #Innovation  #ArtificialIntelligence #ML #ThoughtLeadership #NiteshRastogiInsights 

Rodney Brooks, robotics legend and iRobot co-founder, just spoke at Stanford about our current AI hype cycle. His blunt take: We're repeating the same mistakes. On hype cycles: We're like "Five-year-olds playing soccer—they all run to the ball. Nothing else is important." This doesn't just waste money. It redirects entire fields and kills valuable progress. In his opinion, Humanoid robots are flashy demo machines... Not value-creating machines. His advice: - Investors: Expect 10-30 years from lab to industry, not 18 months - Engineers: Don't abandon long-term vision for funding trends - Public: Be sceptical of demos without real deployment data The winners ignore the hype cycle, they don't chase it. What's your take—is Brooks right about the hype?