facebookresearch / Differentiable Robot Model
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differentiable robot model
Differentiable and learnable robot model. Our differentiable robot model implements computations such as
forward kinematics and inverse dynamics, in a fully differentiable way. We also allow to specify
parameters (kinematics or dynamics parameters), which can then be identified from data (see examples folder).
Currently, our code should work with any kinematic chain. This package comes with wrappers specifically for:
- Kuka iiwa
- Franka Panda
- a 2-link toy robot
Setup
Requirements: python>= 3.7
you can either pip install:
pip install differentiable-robot-model
or clone this repo and install from source:
git clone [email protected]:facebookresearch/differentiable-robot-model.git
cd differentiable-robot-model
python setup.py develop
Examples
2 examples scripts show the learning of kinematics parameters
python examples/learn_kinematics_of_iiwa.py
and the learning of dynamics parameters
python examples/learn_dynamics_of_iiwa.py
L4DC paper and experiments
the notebook experiments/l4dc-sim-experiments
shows a set of experiments that are similar to what we presented
in our L4DC paper
@InProceedings{pmlr-v120-sutanto20a,
title = {Encoding Physical Constraints in Differentiable Newton-Euler Algorithm},
author = {Sutanto, Giovanni and Wang, Austin and Lin, Yixin and Mukadam, Mustafa and Sukhatme, Gaurav and Rai, Akshara and Meier, Franziska},
pages = {804--813},
year = {2020},
editor = {Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger},
volume = {120},
series = {Proceedings of Machine Learning Research},
address = {The Cloud}, month = {10--11 Jun},
publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/sutanto20a/sutanto20a.pdf},
url = {http://proceedings.mlr.press/v120/sutanto20a.html},
}
Testing
running pytest
in the top-level folder will run our differentiable robot model tests,
which compare computations against pybullet.
License
differentiable-robot-model
is released under the MIT license. See LICENSE for additional details about it.
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