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 trees. This package comes with wrappers specifically for:
- TriFinger Edu
- Kuka iiwa
- Franka Panda
- Allegro Hand
- Fetch Arm
- a 2-link toy robot
You can find the documentation here: Differentiable-Robot-Model Documentation
Requirements: python>= 3.7
clone this repo and install from source:
git clone git@github.com:facebookresearch/differentiable-robot-model.git
cd differentiable-robot-model
python setup.py develop
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
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},
}
running pytest in the top-level folder will run our differentiable robot model tests,
which compare computations against pybullet.
We enforce linters for our code. The formatting test will not pass if your code does not conform.
To make this easy for yourself, you can either
- Add the formattings to your IDE
- Install the git pre-commit hooks by running
pip install pre-commit pre-commit install
For Python code, use black.
To enforce this in VSCode, install black, set your Python formatter to black and set Format On Save to true.
To format manually, run: black .
differentiable-robot-model is released under the MIT license. See LICENSE for additional details about it.
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