Add PI-ADHERENT framework to ML #889
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First of all well done 🎉 the code is a good shape :) I added some comments in the PR. Here are some general comments
- I would avoid adding ONNX here. You can approach the problem as it is done for MANN. The ONNX model is downloaded from Hugging Face during project compilation
- We may call the classes PIMANN instead of VelMANN (something we can discuss F2F)
- there are some leftovers in the comments
bindings/python/ML/include/BipedalLocomotion/bindings/ML/velMANN.h
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bindings/python/ML/include/BipedalLocomotion/bindings/ML/velMANN.h
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bindings/python/ML/include/BipedalLocomotion/bindings/ML/velMANN.h
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src/ML/include/BipedalLocomotion/ML/velMANNAutoregressiveInputBuilder.h
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devices/YarpRobotLoggerDevice/app/robots/ergoCubGazeboV1_1/yarp-robot-logger.xml
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GiulioRomualdi
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Oct 11, 2024
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The PR will be automatically merged when the CI ends |
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Thank you for the effort @evelyd 🎉 |
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These changes comprise all of the implementation of the velocity-based features, use of PI-ADHERENT trained models, and trajectory generation capability for the aforementioned model.
I also made a small fix to the Python binding of the
change_fixed_framefunction, because the binding doesn't work otherwise (see #310).