Robust Visual Sim-to-Real Transfer for Robotic Manipulation
Ricardo Garcia,Robin Strudel,Shizhe Chen,Etienne Arlaud,Ivan Laptev,Cordelia Schmid,Ricardo Garcia,Robin Strudel,Shizhe Chen,Etienne Arlaud,Ivan Laptev,Cordelia Schmid
Learning visuomotor policies in simulation is much safer and cheaper than in the real world. However, due to discrepancies between the simulated and real data, simulator-trained policies often fail when transferred to real robots. One common approach to bridge the visual sim-to-real domain gap is domain randomization (DR). While previous work mainly evaluates DR for disembodied tasks, such as pose...
