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Since Brax is fully differentiable, I thought it'd be possible to use it like DiffTaichi or GradSim for system identification (e.g. determining the mass of an object from a trajectory and known force) but I couldn't find any example for this.
Do you happen to have any demo or tips for this?
From the top of my head I would do something like this:
Let's say the task is to estimate the mass of a cube that received a push. The size of the cube is known, same as the friction coefficient, and the applied force.
Record a rollout of the positions of that cube after the push.
Reset the cube to its starting position and set the mass to a random value.
(a) generating a rollout, (b) measuring the MSE between new observed positions over time and GT positions, (c) calculate the gradient wrt to the mass property of the cube and applying that to the mass.
Repeat (3) until the loss increases.
Best,
Florian
The text was updated successfully, but these errors were encountered:
fgolemo
changed the title
Demo request: optimizing for mass
Demo request: optimizing for mass/system identification
Sep 28, 2021
Dear Brax team,
Since Brax is fully differentiable, I thought it'd be possible to use it like DiffTaichi or GradSim for system identification (e.g. determining the mass of an object from a trajectory and known force) but I couldn't find any example for this.
Do you happen to have any demo or tips for this?
From the top of my head I would do something like this:
Let's say the task is to estimate the mass of a cube that received a push. The size of the cube is known, same as the friction coefficient, and the applied force.
Best,
Florian
The text was updated successfully, but these errors were encountered: