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Our next choice is simulators, which can successfully deal with all the aforementioned problems pretty well. In the shift from Internet AI to embodied AI, simulators take the role that was previously played by traditional datasets. Additionally, one more advantage of using simulators is that the physics in the environment can be tweaked as well. For instance, some traditional approaches in this field [64] are sensitive to noise, and for the remedy, the noise in the sensors can be turned off for the purpose of this task.

As a result, agents nowadays are often developed and benchmarked in simulators [65, 66], and once a promising model has been trained and tested, it can then be transferred to the physical world [67, 68].

House3D [69], AI2‐THOR [70], Gibson [71], CHALET [72], MINOS [73], and Habitat [74] are some of the popular simulators for the embodied AI studies. These platforms vary with respect to the 3D environments they use, the tasks they can handle, and the evaluation protocols they provide. These simulators support different sensors such as vision, depth, touch, and semantic segmentation.