  Policies trained in simulation often fail when transferred to the real world
due to the `reality gap' where the simulator is unable to accurately capture
the dynamics and visual properties of the real world. Current approaches to
tackle this problem, such as domain randomization, require prior knowledge and
engineering to determine how much to randomize system parameters in order to
learn a policy that is robust to sim-to-real transfer while also not being too
conservative. We propose a method for automatically tuning simulator system
parameters to match the real world using only raw RGB images of the real world
without the need to define rewards or estimate state. Our key insight is to
reframe the auto-tuning of parameters as a search problem where we iteratively
shift the simulation system parameters to approach the real-world system
parameters. We propose a Search Param Model (SPM) that, given a sequence of
observations and actions and a set of system parameters, predicts whether the
given parameters are higher or lower than the true parameters used to generate
the observations. We evaluate our method on multiple robotic control tasks in
both sim-to-sim and sim-to-real transfer, demonstrating significant improvement
over naive domain randomization. Project videos and code at
https://yuqingd.github.io/autotuned-sim2real/
