[](https://github.com/lumoe/dlkinematics/actions/workflows/main.yml)
# Deep Learning Kinematics
### Differentiable Forwad Kinematics for TensorFlow and Keras
Supported Joint Types:
- [x] Fixed
- [x] Revolute
- [x] Continious
- [x] Prismatic
- [x] Floating (not coverd by unit tests)
- [x] Planar (not coverd by unit tests)
## Installation
### Install from PyPi
`$ pip install dlkinematics`
### Install from source
`$ pip install -e git+https://github.com/lumoe/dlkinematics.git@main#egg=DLKinematics`
## Usage:
```python
import tensorflow as tf
from dlkinematics.urdf import chain_from_urdf_file
from dlkinematics.dlkinematics import DLKinematics
# Load URDF
chain = chain_from_urdf_file('data/human.urdf')
# Create DLKinematics
dlkinematics = DLKinematics(
chain,
base_link="human_base",
end_link="human_spine_2",
batch_size=2)
# Joint configuartion
thetas = tf.Variable([1., 2., 3., 4.], dtype=tf.float32)
# Forward pass
with tf.GradientTape() as tape:
result = dlkinematics.forward(thetas)
print(result)
print(tape.gradient(result, thetas))
```
## As Keras Layer
```python
from dlkinematics.training_utils import ForwardKinematics
from tensorflow import keras
import tensorflow as tf
model = keras.Sequential()
FK_layer = ForwardKinematics(
urdf_file = 'path/to/urdf',
base_link = 'link0',
end_link = 'linkN',
batch_size = 2)
model.add(FK_layer)
# Output shape of FK_layer is (batch_size, 4, 4)
```
## Run tests
The tests use ROS packages to validate the result of the dlkinematics module.
1. Build the docker image for tests:
`$ docker build -t dlkinematics_tests .`
1. Start the container in the root folder of the project:
`$ docker run -it -v $PWD:/work dlkinematics_tests python3 -m pytest`
1. Execute all tests:
`$ docker run -it -v $PWD:/work dlkinematics_tests python3 -m pytest`
Execute only a single testfile:
`$ docker run -it -v $PWD:/work dlkinematics_tests python3 -m pytest tests/test_prismatic.py`
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"description": "[](https://github.com/lumoe/dlkinematics/actions/workflows/main.yml)\n\n# Deep Learning Kinematics\n\n### Differentiable Forwad Kinematics for TensorFlow and Keras\n\nSupported Joint Types:\n\n- [x] Fixed\n- [x] Revolute\n- [x] Continious\n- [x] Prismatic\n- [x] Floating (not coverd by unit tests)\n- [x] Planar (not coverd by unit tests)\n\n## Installation\n\n### Install from PyPi\n\n`$ pip install dlkinematics`\n\n### Install from source\n\n`$ pip install -e git+https://github.com/lumoe/dlkinematics.git@main#egg=DLKinematics`\n\n## Usage:\n\n```python\nimport tensorflow as tf\nfrom dlkinematics.urdf import chain_from_urdf_file\nfrom dlkinematics.dlkinematics import DLKinematics\n\n# Load URDF\nchain = chain_from_urdf_file('data/human.urdf')\n\n# Create DLKinematics\ndlkinematics = DLKinematics(\n chain,\n base_link=\"human_base\",\n end_link=\"human_spine_2\",\n batch_size=2)\n\n# Joint configuartion\nthetas = tf.Variable([1., 2., 3., 4.], dtype=tf.float32)\n\n# Forward pass\nwith tf.GradientTape() as tape:\n result = dlkinematics.forward(thetas)\n\nprint(result)\nprint(tape.gradient(result, thetas))\n\n```\n\n## As Keras Layer\n\n```python\nfrom dlkinematics.training_utils import ForwardKinematics\nfrom tensorflow import keras\nimport tensorflow as tf\n\nmodel = keras.Sequential()\n\nFK_layer = ForwardKinematics(\n urdf_file = 'path/to/urdf',\n base_link = 'link0',\n end_link = 'linkN',\n batch_size = 2)\n\nmodel.add(FK_layer)\n# Output shape of FK_layer is (batch_size, 4, 4)\n```\n\n## Run tests\n\nThe tests use ROS packages to validate the result of the dlkinematics module.\n\n1. Build the docker image for tests: \n `$ docker build -t dlkinematics_tests .`\n\n1. Start the container in the root folder of the project: \n `$ docker run -it -v $PWD:/work dlkinematics_tests python3 -m pytest`\n\n1. Execute all tests: \n `$ docker run -it -v $PWD:/work dlkinematics_tests python3 -m pytest` \n Execute only a single testfile: \n `$ docker run -it -v $PWD:/work dlkinematics_tests python3 -m pytest tests/test_prismatic.py`\n",
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