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What's different between these two BC policies? It's the same architecture, training budget, and data collection setup — the only difference is the controller gains! Controller gains are an understudied design parameter in robot learning. In our new work (w/ antonia bronars*, Pulkit Agrawal), we show how they act... show more

Younghyo Park

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153,393 просмотров • 2 месяцев назад •via X (Twitter)

Образование Наука и технологии

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How can we more effectively leverage robot data from different embodiments for skill transfer? Excited to share that our new work, RoVi-Aug, has been accepted to Conference on Robot Learning as an oral paper！ WIth RoVi-Aug, you can augment an existing robot dataset into a different robot and different viewpoints. A policy trained on the augmented dataset can zero-shot deploy on the unseen target robot with significantly different camera angles! 🧵👇 🔗 Check out our paper:

How can we more effectively leverage robot data from different embodiments for skill transfer? Excited to share that our new work, RoVi-Aug, has been accepted to Conference on Robot Learning as an oral paper！ WIth RoVi-Aug, you can augment an existing robot dataset into a different robot and different viewpoints. A policy trained on the augmented dataset can zero-shot deploy on the unseen target robot with significantly different camera angles! 🧵👇 🔗 Check out our paper:

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I FOUND AN INPUT FOR RAGING DEMON (I cant believe I'm actually labbing in MH...) When there's a wound > ⏹️+⏹️ +🔼 +⏺️ Here's also my camera focusing on the controller to show how it works THIS IS CRAZY, ARE WE PLAYING SF6?!

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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

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Shreyas Gite

34,751 просмотров • 1 год назад

When I play Splatoon, I consistently have an issue where my controller gyro fights me and moves left or right by itself, as shown in the clip. This has happened on multiple controllers, with different Switches, so it's clearly an issue with my setup. Has anyone else had this issue, and if so, were you able to fix it?

When I play Splatoon, I consistently have an issue where my controller gyro fights me and moves left or right by itself, as shown in the clip. This has happened on multiple controllers, with different Switches, so it's clearly an issue with my setup. Has anyone else had this issue, and if so, were you able to fix it?

Chase247

30,408 просмотров • 2 лет назад