{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bridging-the-sim2real-gap-vision-encoder-pre","title":"Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer","arxiv_id":"2501.16389","date":"2025-01-26","proceeding":null,"authors":["Samuel Biruduganti","Yash Yardi","Lars Ankile"],"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","url_abs":"https://arxiv.org/abs/2501.16389v1","url_pdf":"https://arxiv.org/pdf/2501.16389v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bridging-the-sim2real-gap-vision-encoder-pre","repo_url":"https://github.com/yyardi/bridging-the-sim2real-gap","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}