Sep Week 3- Robotix Pulse

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It doesn’t flip or do parkour. But this ‘𝐛𝐨𝐫𝐢𝐧𝐠’ video may be a 𝐬𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 milestone toward 𝐠𝐞𝐧𝐞𝐫𝐚𝐥-𝐩𝐮𝐫𝐩𝐨𝐬𝐞 𝐡𝐮𝐦𝐚𝐧𝐨𝐢𝐝𝐬! Here is the tech 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝: The big idea in the new work from Boston Dynamics and Toyota Research Institute is the use of a Large Behavior Model (LBM). 𝐖𝐡𝐚𝐭 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐢𝐬 𝐚𝐧 𝐋𝐁𝐌? An LBM is an AI foundation model that translates multimodal data, such as language and visual information, into a wide range of robot actions. Similar to how large language models (LLMs), like ChatGPT, learn from text, LBMs are trained on massive, diverse datasets of human and robot demonstrations to develop generalizable skills. 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐰𝐨𝐫𝐤 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭? - The traditional belief was that robots make great specialists but 𝐩𝐨𝐨𝐫 𝐠𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐬𝐭𝐬. They could do one job well, but they often failed if you changed the object, the setup, or the environment. LBM 𝐟𝐥𝐢𝐩𝐬 that belief. By combining demonstrations across many tasks and robots, they become generalists — able to adapt when things change, outperforming from-scratch single-task training models [Ref: https://lnkd.in/eQCvqG9P] - This is the result of a huge collaboration. The Open-X Embodiment project (Google DeepMind + 33 𝐭𝐨𝐩 academic institution labs, such as Carnegie melon, Stanford, Berkeley, pooled demonstrations from 22 robot types into one dataset. A single model trained on this diverse data, called RT-1-X, beat task-specific models by 50%, huge jump. [ref: https://lnkd.in/ev8JRPYG] 𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐢𝐬 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥: - Resilience → If something goes wrong — a part slips, a lid closes — the robot can adapt and recover, rather than stop. - Full-body use → The robot isn’t just moving arms; it’s taking steps, crouching, shifting balance, and coordinating its whole body. - Scalable → Any task that can be demonstrated by a human operator can be added to the training pool, so the model keeps getting stronger. Ref: - https://lnkd.in/ea6tAjBv - https://lnkd.in/ebN-ZEaw 𝐁𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: LBMs represent a shift from “one robot, one task” to data-driven, general-purpose skills. Just like LLMs transformed how machines handle language, LBMs could be the turning point for how robots handle the physical world. 𝐓𝐡𝐢𝐬 𝐩𝐨𝐬𝐭 𝐢𝐬 𝐬𝐩𝐨𝐧𝐬𝐨𝐫𝐞𝐝 𝐛𝐲 Udacity. Check out Udacity’s 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 Nanodegree: real-world projects with hands-on guidance from industry experts: https://lnkd.in/ekdKg5DT Udacity helps professionals build tech and AI skills through projects, mentorship, and career support. Start learning today! *Disclosure: This post includes affiliate links. They support my work at no extra cost to you, but you’re never obligated to use them. **Video Credit: Boston Dynamics & TRI

𝗔𝗜 is rewriting the rules of software development — & a lot of us 𝗮𝗿𝗲 𝘄𝗼𝗿𝗿𝗶𝗲𝗱. how do we 𝗰𝗼𝗽𝗲 𝗮𝗻𝗱 𝘀𝘁𝗮𝘆 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁? Here’s how one may approach it: 1. Health comes first Exercise, diet, and mental health are not optional. McKinsey found that burnout is one of the biggest risks in tech careers. 2. Strengthen the fundamentals Math, algorithms, and computer science basics will outlast any framework or AI tool. MIT research shows engineers with strong foundations pick up new tools 2–3x faster. 3. Develop human skills Connection, leadership, and collaboration matter more than ever. AI can generate code, but it cannot build trust with a team or lead through uncertainty. 4. Think at a higher level System design and architecture separate senior engineers from juniors. Google’s research on technical leadership highlights that engineers who can design at scale remain indispensable. 5. Learn how to learn Companies will hire engineers who can quickly adapt. World Economic Forum ranked “active learning” and “complex problem-solving” among the top future skills for 2025. 6. Connect disciplines Robotics, medicine, biology, and ML are converging. Innovation often happens at the edges where fields intersect. My own work in robotics has shown me that breakthroughs rarely stay in one silo. 👉 How are you preparing for the AI shift in software engineering?

You could do everything right and still lose. You could mess up and still win. That’s the reality of risk and luck— outcomes don’t always follow effort. The only thing you control is how many smart bets you make