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A novel representation learning framework that emphasizes the importance of 3D spatial awareness in embodied AI.

Abstract: A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown promise in transferring trained policies to new tasks based on demonstrations.

Abstract: Neural Radiance Fields (NeRFs), despite their outstanding performance on novel view synthesis, often need dense input views. Many papers train one model for each scene respectively and few of them explore incorporating multimodal data into this problem.

Building open-ended agents with internet-scale knowledge in Minecraft.

Abstract： 3D subtomogram image alignment, clustering, and segmentation are vital to macromolecular structure recognition in cryo-electron tomography (cryo-ET). However, acquiring ground-truth labels to train a unified deep learning model that can simultaneously deal with these tasks is unaffordable.