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Here’s something you didn’t know LLMs can do – reconstruct articulated objects! Introducing Real2Code – our new real2sim approach that scalably reconstructs complex, multi-part articulated objects.

Mandi Zhao

4,878 subscribers

91,867 просмотров • 2 лет назад •via X (Twitter)

Образование Новости и политика Наука и технологии

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Wouldn’t it be nice if ChatGPT could find your missing keys for you? Our latest research from Berkeley AI Research + Google AI suggests that robots can use large language models (LLMs) to find hidden objects faster. 🧵👇

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Applying to university or know someone who is? ALX is introducing a new 3-Year Tech programme that will qualify you for a degree. Graduates will not only earn a certificate from ALX, but will also earn academic credits that can transferred to one our global partner universities.

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It pains me to know that the belief is that a therapist role is to "validate". I do not validate. If you are with me in a clinical setting and what you're saying doesn't make any damn sense, I'm going to have you walk me through it. What I've discovered is that a lot of the dysfunction people have is that they've never articulated their beliefs and so have no idea how absurd they are. Simply going through the exercise of having to explain yourself can be deeply rewarding. It's something we call "accountability". You literally account for yourself. Sure, I've had some female clients chimp out and flee never to be seen again at their first contact with a mirror. I've had many others weep openly that actually being held psychologically. One I will never get out of my mind was a wonderful 30-something year old woman who cried and said, "I have been in therapy for 7 years. Why has nobody ever challenged me and help me see that I'm the one causing all this shit?" That's an important question. And it's crucial that if you see a therapist, you don't see a charlatan. You can tell the good ones by their headgear.

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Is your toddler suddenly throwing (or dropping) everything they get their hands on? Food, toys… you name it. You’re not alone. This week I’ve been introducing play schemas, 9 common patterns that can help to demystify your toddler’s seemingly random behaviors. And you guessed it: this is one. We call it the trajectory schema. If you take nothing else away from this post, let it be this: your little one doesn’t throw things because they are misbehaving or “bad.” Babies throw things because they are babies. And they are learning as they do so. Learning about cause and effect. Learning about gravity. Learning what (and practicing something new) they can do with their bodies. Learning hand-eye coordination. It’s a completely normal part of development. And the fascination won’t last forever. The real question is, how do you manage it? First, be proactive. Expect that anything your little one handles might reasonably be thrown or dropped. So choose wisely, avoiding items that are valuable or might pose a danger to them or others if they suddenly took flight. Second, depending on the age of your child, begin introducing some natural consequences. Put the toy/object away temporarily after it is thrown (if throwing it is dangerous or inappropriate). And involve your child in the subsequent clean-up. You can also redirect. Explain which things are and are not for throwing. Model the correct use of these objects. Finally, lean into it. Recognizing that young children are drawn to throwing, provide items that are safe and acceptable to throw. Soft toys. Socks. Balls. And look for opportunities and settings for your little one to explore this urge safely and appropriately. How did you manage your toddler’s throwing phase? Welcome your tips and tricks! This sweet little throwing machine was posted to TT by kristinnicole122.

Is your toddler suddenly throwing (or dropping) everything they get their hands on? Food, toys… you name it. You’re not alone. This week I’ve been introducing play schemas, 9 common patterns that can help to demystify your toddler’s seemingly random behaviors. And you guessed it: this is one. We call it the trajectory schema. If you take nothing else away from this post, let it be this: your little one doesn’t throw things because they are misbehaving or “bad.” Babies throw things because they are babies. And they are learning as they do so. Learning about cause and effect. Learning about gravity. Learning what (and practicing something new) they can do with their bodies. Learning hand-eye coordination. It’s a completely normal part of development. And the fascination won’t last forever. The real question is, how do you manage it? First, be proactive. Expect that anything your little one handles might reasonably be thrown or dropped. So choose wisely, avoiding items that are valuable or might pose a danger to them or others if they suddenly took flight. Second, depending on the age of your child, begin introducing some natural consequences. Put the toy/object away temporarily after it is thrown (if throwing it is dangerous or inappropriate). And involve your child in the subsequent clean-up. You can also redirect. Explain which things are and are not for throwing. Model the correct use of these objects. Finally, lean into it. Recognizing that young children are drawn to throwing, provide items that are safe and acceptable to throw. Soft toys. Socks. Balls. And look for opportunities and settings for your little one to explore this urge safely and appropriately. How did you manage your toddler’s throwing phase? Welcome your tips and tricks! This sweet little throwing machine was posted to TT by kristinnicole122.

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168,975 просмотров • 2 лет назад

Pi0 vs. ACT with BBox conditioning 🟦 Not many know you can push ACT to *almost-Pi0* generalisation by conditioning on bounding boxes (BBoxes). How is the training data collected? • Generate BBoxes for all pick-and-place objects in the scene.(I used Gemini) • Pick-and-place targets are selected randomly. • Add the BBox coordinates to the robot’s state. • Overlay the BBoxes in the visualisation so you know what to grab and where to drop. During inference: • Generate BBoxes for every object again. • Click the object you want to pick and its target spot; those BBoxes get added to the robot state. • Let the robot do the work for you 😃 Setup: - Trained ACT for 100k steps and fine-tuned Pi0 for only 20k. - Training data is 60 episodes and had *only* LEGO bricks. - Using single front camera (Laptop in this case) Got the idea from xun in LeRobot discord. Here’s ACT vs Pi0 on a toy car that isn’t in the dataset. 1/3

Pi0 vs. ACT with BBox conditioning 🟦 Not many know you can push ACT to almost-Pi0 generalisation by conditioning on bounding boxes (BBoxes). How is the training data collected? • Generate BBoxes for all pick-and-place objects in the scene.(I used Gemini) • Pick-and-place targets are selected randomly. • Add the BBox coordinates to the robot’s state. • Overlay the BBoxes in the visualisation so you know what to grab and where to drop. During inference: • Generate BBoxes for every object again. • Click the object you want to pick and its target spot; those BBoxes get added to the robot state. • Let the robot do the work for you 😃 Setup: - Trained ACT for 100k steps and fine-tuned Pi0 for only 20k. - Training data is 60 episodes and had only LEGO bricks. - Using single front camera (Laptop in this case) Got the idea from xun in LeRobot discord. Here’s ACT vs Pi0 on a toy car that isn’t in the dataset. 1/3

Shreyas Gite

34,863 просмотров • 1 год назад