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Image2Sim is a neural simulation framework that creates high-fidelity interactive environments from RGB-D images, enabling scalable training for embodied navigation agents. It generates nearly 20K scenes and over 10 million training samples, showing strong benchmark improvements and effective real-world zero-shot transfer.
RynnWorld-4D is a generative world model that co-produces future RGB, depth, and optical flow from a single RGB-D image and language instruction using a unified diffusion process, enabling efficient robotic manipulation through inverse dynamics policy learning. It achieves state-of-the-art on real-world bimanual manipulation tasks.
A flow-matching model generates diverse human grasps from RGB-D images, enabling zero-shot robotic grasping with improved performance over existing methods. The model, trained on a large egocentric dataset, significantly outperforms state-of-the-art baselines on a new benchmark.
This paper introduces Geometric Primary Structure (GPS), a new representation for articulated parts perception in robot manipulation, enabling efficient VR-based annotation and achieving a 73% success rate without fine-tuning.
AFUN proposes an affordance foundation model that predicts functional masks and 3D motion curves from RGB-D observations and language descriptions, enabling generalizable robot manipulation across diverse environments. The model outperforms baselines on multiple benchmarks and can be deployed for real-world tasks without fine-tuning.
This paper proposes COVER, a training-free method for converting 3D assets into sparse panoramic RGB-D-pose data with complete scene coverage and low redundancy, and introduces the CM-EVS dataset containing 36,373 curated frames from indoor and outdoor scenes.