Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer
This work addresses the Sim2Real distribution shift problem for roboticists, offering an incremental evaluation framework to improve policy transferability.
The paper tackled the simulation-to-reality gap in robotic policy transfer by evaluating large-scale pre-trained vision encoders, finding that manipulation-pretrained encoders achieve higher action scores and CNNs show stronger domain invariance than ViTs, with the best models combining both properties.
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.