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Dear Sentinels
What a week it has been! As you may recall, I started at the University of Southampton earlier this month, and my first deliverable is looming, just four days away, not that I’m counting. Of course, as I mentioned when I broke the news about the new job, I wrote this last week, so if anything goes wrong, blame my past self, arg! I’ve also thrown my hat in the ring for five years of funding, fingers crossed, and I’ll keep you posted if the universe is feeling generous. This week, we’re dipping our toes into the world of Large Language Models for robotics. Exciting stuff, and no robots were harmed in the making of this edition.
Vision–language–action (VLA) models are causing quite the stir in the world of robotics. Gone are the days when robots needed separate systems for seeing, thinking, and moving, VLA models bundle it all together in one neat package. The secret sauce? They train on camera feeds, natural language instructions, and the resulting actions, all at the same time. This means our robot friends can finally connect what they see, what you say, and what they’re supposed to do, no more endless lines of task-specific code (and no more late-night debugging sessions, thank goodness). The real magic happens when you feed these models mountains of data from the internet, giving them a surprisingly broad grasp of the world. Add a dash of imitation or reinforcement learning, and suddenly they’re not just moving, but actually figuring things out in the real world. The upshot: robots that aren’t doomed to a life of repetitive tasks, but can adapt on the fly to new places, objects, and jobs, even if they’ve never seen them before.
In the investigative article up next, we’ll take a closer look at how language and motion are coming together in robotics, no interpretive dance required. After that, the academic article will dig into what really matters when building vision–language–action models for generalist robots. But before we get too serious, let’s see what oddities the web has thrown up for us this week.
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