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аналитика - Visual Imitation Learning
Stem-OB: Enhancing Generalizability in Visual Imitation Learning by Converging Observations through Diffusion Inversion
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.
Bi-KVIL: Key Points-Based Visual Imitation Learning of Bimanual Manipulation Tasks
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: Keypoints-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.
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