Peter Ludwig: preference

Transcript context

…For 10 minutes. The most amazing I can get. I love, I love watching robots do acrobatics like everybody but the these systems actually overheat, right? If, like, And one of the ways you can use simulation though is you can actually have that, the temperature of those actuators be one of the parameters that’s represented in the simulation. And if you’re doing reinforcement learning over a certain task, then the robot can actually adjust its motions in the simulation to account for the fact that, oh, it knows that as it’s moving, it’s actually beginning to overheat this motor. But if you didn’t have that parameter of, let’s say, the heat of that motor represented in the simulation initially, then your RL policy might It will disregard that. And now you run that on the robot and the robot will overheat and fail. I guess the question is, like, how do you have all of these parameters taken care of while also understanding the deployment environment? Like, temperature is like a great example, right? Well why did you make my robot worse when it runs in like a freezer? So it actually shouldn’t worry about that. it’s like, yeah, how do you design these simulations?…

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