Part 2/9:
Training robots to effectively navigate and function in the real world has consistently posed challenges for developers. Traditionally, the methods employed — extensive real-world testing and simplified virtual simulations — leave much to be desired.
While real-world data collection is costly, time-consuming, and fraught with risks, standard simulations often fail to replicate the complexities of real-life scenarios effectively. Factors like irregular lighting, cluttered spaces, and varied surface reflections can derail robot performance. An autonomous vehicle, for example, may excel in a controlled simulated environment but struggle when facing unpredictable conditions such as rain or unexpected pedestrians in real-world situations, highlighting the critical Sim to Real Gap.