Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer

• URL: http://arxiv.org/abs/2501.16389v1
• Date: Sun, 26 Jan 2025 00:27:04 GMT
• Title: Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer
• Authors: Samuel Biruduganti, Yash Yardi, Lars Ankile,
• Abstract summary: "Sim2Real" distribution shift prevents successful policy transfer from simulation to reality. This study explores the potential of using large-scale pre-training of vision encoders to address the Sim2Real gap.
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• Abstract: 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. This study explores the potential of using large-scale pre-training of vision encoders to address the Sim2Real gap. We examine a diverse collection of encoders, evaluating their ability to (1) extract features necessary for robot control while (2) remaining invariant to task-irrelevant environmental variations. We quantitatively measure the encoder's feature extraction capabilities through linear probing and its domain invariance by computing distances between simulation and real-world embedding centroids. Additional qualitative insights are provided through t-SNE plots and GradCAM saliency maps. Findings suggest that encoders pre-trained on manipulation-specific datasets generally outperform those trained on generic datasets in bridging the Sim2Real gap. https://github.com/yyardi/Bridging-the-Sim2Real-Gap

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