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In theory, could I use this (or something like it) to develop algorithms for controlling autonomous vehicles (aerial, ground, and/or aquatic), then if I ever ge

In theory, could I use this (or something like it) to develop algorithms for controlling autonomous vehicles (aerial, ground, and/or aquatic), then if I ever get my experimentation to the point that I’m interested in doing so, building the vehicle itself and deploying it with a reasonable degree of confidence that it would actually work?

jafingi 7y ago
There would be a reality gap, going from simulation to real world. Good read: https://ai.googleblog.com/2017/10/closing-simulation-to-reality-gap-for.html https://ai.googleblog.com/2017/10/closing-simulation-to-real...
- jcims 7y ago
  AKA 'sim2real' gap for googling purposes. related: https://lilianweng.github.io/lil-log/2019/05/05/domain-randomization.html https://lilianweng.github.io/lil-log/2019/05/05/domain-rando...
currymj 7y ago
getting things like this to transfer well is not necessarily easy, it’s a major active area of research. even if you somehow had a perfect physics model, there are problems like the fact that motors and joints behave differently as they wear down, etc. it might be easier for wheeled vehicles — usually the examples of failure are complicated, error-prone things like bipedal walking, hands with lots of joints.
- lutorm 7y ago
  Yeah the trick is knowing what the performance of the as-built vehicle will be before building it. You can to some extent marginalize over this by running randomized trials with different parameters for your mechanisms so that you verify that you're not too sensitive to the exact setup. The control system needs to be fairly robust to such variations. But you still don't know that you've modeled all the physics correctly until it's actually flight-proven. Picking the correct degree of fidelity in a simulation model isn't super straightforward.
- TaylorAlexander 7y ago
  My thinking is that if your sim is kinda realistic, then once it works you can fine tune on some (expensive) real hand labeled data. But the sim helps you develop the training process etc.

jafingi 7y ago

There would be a reality gap, going from simulation to real world. Good read: https://ai.googleblog.com/2017/10/closing-simulation-to-reality-gap-for.html https://ai.googleblog.com/2017/10/closing-simulation-to-real...

jcims 7y ago
AKA 'sim2real' gap for googling purposes. related: https://lilianweng.github.io/lil-log/2019/05/05/domain-randomization.html https://lilianweng.github.io/lil-log/2019/05/05/domain-rando...

currymj 7y ago

getting things like this to transfer well is not necessarily easy, it’s a major active area of research. even if you somehow had a perfect physics model, there are problems like the fact that motors and joints behave differently as they wear down, etc. it might be easier for wheeled vehicles — usually the examples of failure are complicated, error-prone things like bipedal walking, hands with lots of joints.

lutorm 7y ago
Yeah the trick is knowing what the performance of the as-built vehicle will be before building it. You can to some extent marginalize over this by running randomized trials with different parameters for your mechanisms so that you verify that you're not too sensitive to the exact setup. The control system needs to be fairly robust to such variations. But you still don't know that you've modeled all the physics correctly until it's actually flight-proven. Picking the correct degree of fidelity in a simulation model isn't super straightforward.
TaylorAlexander 7y ago
My thinking is that if your sim is kinda realistic, then once it works you can fine tune on some (expensive) real hand labeled data. But the sim helps you develop the training process etc.