Long Le @LongLeRobot
PhD student @Penn. Working on robot learning. vlongle.github.io Philadelphia, USA Joined June 2018-
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Le et al., "Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels" Feed-forward mapping of material property from CLIP, powered by synthetic data. Can be distilled to 3D representations (eg NeRF/3DGS) to do some cool physics stuff.
Check out our recent work on fast physical property prediction for multi-material objects from images, led by our amazing student @LongLeRobot ! We’ve also released our annotations and data generation pipeline (github.com/vlongle/pixie) for 3D objects with physical material labels…
Check out our recent work on fast physical property prediction for multi-material objects from images, led by our amazing student @LongLeRobot ! We’ve also released our annotations and data generation pipeline (github.com/vlongle/pixie) for 3D objects with physical material labels…
Those who works in robot sim2real knows that the visual gap is very real and non-trivial 😤 so it was very exciting to see Pixie zero-shot generalize to real-world scenes out of the box! (P.S. I also had a lot of fun making these web visualization @ pixie-3d.github.io)
Those who works in robot sim2real knows that the visual gap is very real and non-trivial 😤 so it was very exciting to see Pixie zero-shot generalize to real-world scenes out of the box! (P.S. I also had a lot of fun making these web visualization @ pixie-3d.github.io) https://t.co/WAitAi3HYP
My dreams are in R&D labs.
Exciting work on producing 3D physics simulation by @LongLeRobot!
Exciting work on producing 3D physics simulation by @LongLeRobot!
The first step in “world models” has been visual modeling—great for flashy demos, but robots need more than watching the world, they need to act in it. Visuals may aid semantics & planning, yet it’s physics that dictates complex object interactions. Excited to see @LongLeRobot…
The first step in “world models” has been visual modeling—great for flashy demos, but robots need more than watching the world, they need to act in it. Visuals may aid semantics & planning, yet it’s physics that dictates complex object interactions. Excited to see @LongLeRobot…
Dyna has done it again! Continuous demo, no tricks!
Dyna has done it again! Continuous demo, no tricks!
As promised: three commercially valuable tasks continuously demoed live for all attendees Public demos are a big deal -- there's no reshoots or camera tricks to hide flaws Our stack is simply robust enough to set up multiple bots and showcase valuable manipulation skills live
As promised: three commercially valuable tasks continuously demoed live for all attendees Public demos are a big deal -- there's no reshoots or camera tricks to hide flaws Our stack is simply robust enough to set up multiple bots and showcase valuable manipulation skills live https://t.co/ZfQrCXciQb
Thank you @Almorgand for sharing our work!
Thank you @Almorgand for sharing our work!
"Pixie: Physics from Pixels" TL;DR: NeRF, GS w/ physics; neural network mapping pretrained visual features (i.e., CLIP) to dense material fields of physical properties in a single forward pass, enabling real‑time physics simulations.
Thanks AK for sharing our work! 😊
Pixie Fast and Generalizable Supervised Learning of 3D Physics from Pixels
Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels Contributions: 1. Novel Framework for 3D Physics Prediction: We introduce PIXIE, a unified framework that predicts discrete material types and continuous physical parameters (Young’s modulus, Poisson’s…
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