Together, the papers span the full stack of challenges robot developers face: coordinating multiple arms in parallel, building policies that generalize across robot bodies, grasping novel objects in clutter, performing precise assembly and developing vision-language-action models that reason before they move.

The throughline is clear: sim-to-real is becoming a foundation for robots that can adapt, generalize, and operate with greater reliability outside the lab.

Picture a pharmaceutical lab run by robotic arms: picking up tubes, transferring liquids, mixing reagents — each step taking different amounts of time, all requiring careful coordination.

Traditional robot scheduling software handles those steps sequentially, one arm at a time.