الملخص
Rebar insertion is among the most repetitive and physically demanding tasks on construction sites, and a contact-rich problem at 1.4 mm clearance. The parts, however, vary at two levels: a nominal design per structural member, and fabrication tolerance around each nominal design. Real-world data therefore has to be re-collected as designs and batches change. We present RebarSim, a visual sim-to-real system trained entirely in simulation. A privileged state-based teacher is trained with reinforcement learning over procedurally generated rebar geometries, then distilled into a multi-view student that maps raw RGB and proprioception directly to actions under extensive domain randomization. The student transfers to the real world zero-shot, seating rebars taken from a real factory production run in 91.3% of real-robot rollouts. Underlying that result, geometry diversity and pretraining both bring benefits. Training across a diverse set of nominal designs rather than one lifts the zero-shot success of both the teacher and the student on unseen designs, and the student policy outperforms a single-design specialist on that specialist's own design. A pretrained student then adapts to a new design with 4--6x fewer distillation samples than one trained from scratch. Visual sim-to-real transfer depends on appearance randomization and the DAgger mixture: removing either one sharply lowers success. Videos, code, and task assets are available at https://rebarsim.github.io.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Sun, T., Han, B., Yin, P., Xu, R., He, H., Gupta, A., Rusinkiewicz, S., & Shao, Y. (2026). Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation. https://omanscience.com/ar/articles/visual-sim-to-real-learning-for-robotic-insertion-under-geometric-variations-application-to-rebar-installation
MLA 9
Sun, Tao, et al. "Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation." https://omanscience.com/ar/articles/visual-sim-to-real-learning-for-robotic-insertion-under-geometric-variations-application-to-rebar-installation.
شيكاغو (المؤلف–التاريخ)
Sun, Tao, Beining Han, Patrick Yin, Rui Xu, Harry He, Abhishek Gupta, Szymon Rusinkiewicz, and Yi Shao. 2026. "Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation." https://omanscience.com/ar/articles/visual-sim-to-real-learning-for-robotic-insertion-under-geometric-variations-application-to-rebar-installation.
هارفارد
Sun, T., Han, B., Yin, P., Xu, R., He, H., Gupta, A., Rusinkiewicz, S. and Shao, Y. (2026) 'Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation', Available at: https://omanscience.com/ar/articles/visual-sim-to-real-learning-for-robotic-insertion-under-geometric-variations-application-to-rebar-installation.
فانكوفر
Sun T, Han B, Yin P, Xu R, He H, Gupta A, et al. Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation. https://omanscience.com/ar/articles/visual-sim-to-real-learning-for-robotic-insertion-under-geometric-variations-application-to-rebar-installation
IEEE
T. Sun, B. Han, P. Yin, R. Xu, H. He, A. Gupta, S. Rusinkiewicz, and Y. Shao, "Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation," https://omanscience.com/ar/articles/visual-sim-to-real-learning-for-robotic-insertion-under-geometric-variations-application-to-rebar-installation.