adam#
Automatic Differentiation for rigid-body-dynamics AlgorithMs
adam implements a collection of algorithms for calculating rigid-body dynamics for floating-base robots, in mixed and body fixed representations using:
adam employs the automatic differentiation capabilities of these frameworks to compute, if needed, gradients, Jacobian, Hessians of rigid-body dynamics quantities. This approach enables the design of optimal control and reinforcement learning strategies in robotics. Thanks to the jax.vmap-ing and jax.jit-ing capabilities, the algorithms can be run on batches of inputs, which are possibly converted to PyTorch using the jax2torch conversion functions.
adam is based on Roy Featherstone’s Rigid Body Dynamics Algorithms.
Examples#
Have a look at the examples folder in the repository!
License#
Getting started: