Sim2Realギャップの橋渡し：視覚エンコーダ事前学習による視覚運動ポリシー転移
Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer
大規模事前学習済み視覚エンコーダがSim2Realギャップを緩和するかをオフライン評価し、操作事前学習エンコーダが高いAction Score、CNNがViTより高いドメイン不変性を示すことを明らかにした。
著者: Yash Yardi, Samuel Biruduganti, Lars Ankile
分類: cs.RO, cs.CV
原文アブストラクト
Simulation offers a scalable and efficient alternative to real-world data collection for learning visuomotor robotic policies. However, the simulation-to-reality, or Sim2Real distribution shift -- introduced by employing simulation-trained policies in real-world environments -- frequently prevents successful policy transfer. We present an offline framework to evaluate the performance of using large-scale pre-trained vision encoders to address the Sim2Real gap. We examine a diverse collection of encoders, assessing their ability to extract features necessary for robot control (Action Score) while remaining invariant to task-irrelevant environmental variations (Domain Invariance Score). Evaluating 23 encoders, we reveal patterns across architectures, pre-training datasets, and parameter scales. Our findings show that manipulation-pretrained encoders consistently achieve higher Action Scores, CNN-based encoders demonstrate stronger domain invariance than ViTs, and the best-performing models combine both properties, underscoring DIS and AS as complementary predictors of Sim2Real transferability.
