クロスウェイ拡散：自己教師あり学習による拡散ベース視覚運動ポリシーの改善
Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning
拡散モデルによるロボット模倣学習において、逆拡散過程の中間表現から画像を再構成する状態デコーダと自己教師あり学習を組み合わせ、視覚表現を強化する手法を提案した。
著者: Xiang Li, Varun Belagali, Jinghuan Shang, Michael S. Ryoo
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Sequence modeling approaches have shown promising results in robot imitation learning. Recently, diffusion models have been adopted for behavioral cloning in a sequence modeling fashion, benefiting from their exceptional capabilities in modeling complex data distributions. The standard diffusion-based policy iteratively generates action sequences from random noise conditioned on the input states. Nonetheless, the model for diffusion policy can be further improved in terms of visual representations. In this work, we propose Crossway Diffusion, a simple yet effective method to enhance diffusion-based visuomotor policy learning via a carefully designed state decoder and an auxiliary self-supervised learning (SSL) objective. The state decoder reconstructs raw image pixels and other state information from the intermediate representations of the reverse diffusion process. The whole model is jointly optimized by the SSL objective and the original diffusion loss. Our experiments demonstrate the effectiveness of Crossway Diffusion in various simulated and real-world robot tasks, confirming its consistent advantages over the standard diffusion-based policy and substantial improvements over the baselines.
