
Stem-OB is a novel preprocessing technique that improves the generalization of visual imitation learning models by leveraging diffusion inversion to converge diverse observations into shared representations, enhancing robustness to visual perturbations without inference-time overhead.


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stem-ob-enhancing-generalizability-in-visual-imitation-learning-by-converging-observations-through-diffusion-inversion


Stem-OB: Enhancing Generalizability in Visual Imitation Learning by Converging Observations through Diffusion Inversion


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ビジュアル模倣学習におけるバイマニュアル操作タスクのキーポイントベースの学習方法を提案する。


bi-kvil-key-points-based-visual-imitation-learning-of-bimanual-manipulation-tasks


Bi-KVIL: Key Points-Based Visual Imitation Learning of Bimanual Manipulation Tasks



Visual imitation learning has made significant progress in unimanual tasks, but bimanual coordination remains a challenge. Bi-KVIL extends keypoints-based visual imitation learning to bimanual manipulation tasks, extracting Hybrid Master-Slave Relationships and task representations.


bi-kvil-keypoints-based-visual-imitation-learning-of-bimanual-manipulation-tasks


Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks
