Stanford Nimble
pip3 install nimblephysics
** BETA SOFTWARE **
Read our docs and the paper.
Use physics as a non-linearity in your neural network. A single timestep, nimble.timestep(state, controls), is a valid PyTorch function.
We support an analytical backwards pass, that works even through contact and friction.
It's as easy as:
from nimble import timestep
# Everything is a PyTorch Tensor, and this is differentiable!!
next_state = timestep(world, current_state, control_forces)Nimble started life as a fork of the popular DART physics engine, with analytical gradients and a PyTorch binding. We've worked hard to maintain as much backwards compatability as we can, so many simulations that worked in DART should translate directly to Nimble.
Check out our website for more information.