Adjusting static weights for each objective about relaxed_ik HOT 2 OPEN

Hi, thanks for the question! The best way to change the relative importance between different objectives is just to change the weight_priors values here . These static weights were set empirically, though many values worked fine and as expected during testing. The weights are unitless and are not on a particular scale; the loss function used was just designed to consider all of the term outputs on a standard scale relative to each other. The main interpretation is just that, when things are going well in the optimization, i.e., most of the term outputs are close to the central “groove” part of the loss function, a term with a weight of 2 is twice as important as a term with a weight of 1.

I would start with adjusting the weight priors on the terms and seeing if any combination matches your desired behavior given the task. If that does not work, feel free to let me know more details about the motion behavior or task you're trying to achieve, and I can maybe provide more recommendations for other parameter adjustments that may get what you’re looking for.

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Hi! @djrakita

As @akgoins mentioned, why does parameter g have a different value in the paper and in the code?

from relaxed_ik.