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Bardienus Duisterhof

@bardienus • 1,478 subscribers

Incoming Postdoc @stanford || PhD Student @CMU_Robotics with @jeff_ichnowski || Efficient World Models for Robot Manipulation 📷 ||

Shorts

Dense tracking of deformable objects can unlock applications in robotics, gen-AI and AR. We present DeformGS (previously MD-Splatting) and release the code and data. Join us at #WAFR where we will present new real-world results! 👇 1/9🧵

Dense tracking of deformable objects can unlock applications in robotics, gen-AI and AR. We present DeformGS (previously MD-Splatting) and release the code and data. Join us at #WAFR where we will present new real-world results! 👇 1/9🧵

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Introducing PointZero—a 3D world model pre-trained without robots. Dexterous manipulation requires understanding diverse 3D dynamics, but current approaches rely on expensive robot data. So how can we pre-train a 3D dynamics model without it? PointZero introduces a simple idea: learning to complete 3D point tracks yields transferable 3D dynamics! Our model achieves SOTA results on: 🔥 Zero-shot 3D dynamics 🔥 Action-conditioned 3D dynamics (post-training) 🔥 Imitation learning (post-training) Details and links 👇 1/6

Introducing PointZero—a 3D world model pre-trained without robots. Dexterous manipulation requires understanding diverse 3D dynamics, but current approaches rely on expensive robot data. So how can we pre-train a 3D dynamics model without it? PointZero introduces a simple idea: learning to complete 3D point tracks yields transferable 3D dynamics! Our model achieves SOTA results on: 🔥 Zero-shot 3D dynamics 🔥 Action-conditioned 3D dynamics (post-training) 🔥 Imitation learning (post-training) Details and links 👇 1/6

Bardienus Duisterhof

29,119 views • 1 month ago

Introducing Modality Forcing, a recipe for post-training T2I models for SOTA RGB-Depth generation! Text-to-image (T2I) models learn rich representations of the spatial world. How do we build on this prior for high-quality depth generation? 🧵 [1/6]

Introducing Modality Forcing, a recipe for post-training T2I models for SOTA RGB-Depth generation! Text-to-image (T2I) models learn rich representations of the spatial world. How do we build on this prior for high-quality depth generation? 🧵 [1/6]

Bardienus Duisterhof

67,032 views • 4 months ago

Imagine if robots could fill in the blanks in cluttered scenes. ✨ Enter RaySt3R: a single masked RGB-D image in, complete 3D out. It infers depth, object masks, and confidence for novel views, and merges the predictions into a single point cloud.

Imagine if robots could fill in the blanks in cluttered scenes. ✨ Enter RaySt3R: a single masked RGB-D image in, complete 3D out. It infers depth, object masks, and confidence for novel views, and merges the predictions into a single point cloud.

Bardienus Duisterhof

43,672 views • 1 year ago

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