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by datahead
1025 days ago
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What is the general outline of going from a model of a craft (drone, sailboat, etc) to building a sim that can do reinforcement learning over a physical object interaction with its env? I want to start playing with models, sims and collected data for sailboat racing- I know the RL/data science stuff, and I assume a good model of your craft takes time to build, and can be improved with collected data. What are some areas to explore when chaining model -> sim -> RL for performance? I realize this is an extremely complex topic, with several PhDs worth of potential input- if you had to explain to someone technical what it looks like and where to keep digging, what would you say? |
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I did not develop the sim itself but did develop the hardware-in-the-loop portion of it along with things like real-time debugging, and output to the hitl. We had the sim rendering cameras which we output from the workstation to custom HDMI bridges over MIPI that we could treat as real cameras on the NVIDIA Xavier AGX. There was a data channel over Ethernet for IMU data.
I made a custom version of Eclipse that interfaced w GDB for debugging, which also was modified to stay in sync w the sim using PTP, w rewind capability.
As for sailboat modeling, yes it’s more complicated because of the effects of both wind and fluid dynamics. If I were approaching this, I’d probably try and find a physics simulator to start with. Getting ground truth will be difficult, but I imagine you would start w the IMU and GPS data off the boat, but having time synced ground truth for the waves and wind will probably be the hardest part.