Dec Week 2 - Robotics Pulse

Generative AI breaks geometry. NeuralRemaster by Toyota Research Institute fixes it for #Robotics. 🔥A massive step for bridging the sim-to-real gap without breaking the laws of physics. The promise of using Generative AI to close the sim-to-real gap is huge. But the reality? Standard diffusion models ruin the geometry. The photorealistic output looks great, but vital structures—like lane lines and obstacles—shift or warp, making the data unusable for #robotics planners The researchers introduced a drop-in replacement called Phase-Preserving Diffusion (ϕ-PD). Instead of total random noise, they use noise that preserves the original image’s phase (the structural skeleton) while only randomizing the magnitude (the appearance). 📍No architectural changes or extra parameters. It works with existing models. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀: Perfect Sim-to-Real: They demonstrated this by converting CARLA simulator (An Open-source simulator for autonomous driving research) images into photorealistic Waymo-style images. Because the geometry stayed locked, the planner performance improved by ~50%. 📰 Paper: https://lnkd.in/ep5fuSrK 👩‍💻 Project Page: https://lnkd.in/er_5tEM5 👥 Authors: Yu Zeng, Charles Ochoa, Mingyuan Zhou, Vishal M. Patel, Vitor Guizilini, and Rowan McAllister --------------------- Interested in a structured path to learn AI? check out Udacity’s fully online 𝗠𝗮𝘀𝘁𝗲𝗿’𝘀 𝗶𝗻 𝗔𝗜: https://lnkd.in/eTMFKN72 [see comment] ---------------------- Thank you Yu Zeng for the permission to use the video.

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