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adam-robotics-0.0.6
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مشاهده بیشترتوضیحات
Automatic Differentiation for rigid-body-dynamics AlgorithMs
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | adam-robotics-0.0.6 |
| نام | adam-robotics |
| نسخه کتابخانه | 0.0.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Giuseppe L'Erario |
| ایمیل نویسنده | gl.giuseppelerario@gmail.com |
| آدرس صفحه اصلی | https://github.com/ami-iit/ADAM |
| آدرس اینترنتی | https://pypi.org/project/adam-robotics/ |
| مجوز | - |
# ADAM
[](https://github.com/ami-iit/ADAM/actions/workflows/tests.yml)
[](https://github.com/ami-iit/ADAM/blob/main/LICENSE)
**Automatic Differentiation for rigid-body-dynamics AlgorithMs**
ADAM implements a collection of algorithms for calculating rigid-body dynamics for **floating-base** robots, in _mixed representation_ (see [Traversaro's A Unified View of the Equations of Motion used for Control Design of Humanoid Robots](https://www.researchgate.net/publication/312200239_A_Unified_View_of_the_Equations_of_Motion_used_for_Control_Design_of_Humanoid_Robots)) using:
- [Jax](https://github.com/google/jax)
- [CasADi](https://web.casadi.org/)
- [PyTorch](https://github.com/pytorch/pytorch)
- [NumPy](https://numpy.org/)
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.
ADAM is based on Roy Featherstone's Rigid Body Dynamics Algorithms.
---
<p align="center">
<b>⚠️ REPOSITORY UNDER DEVELOPMENT ⚠️</b>
<br>We cannot guarantee stable API
</p>
---
## 🐍 Dependencies
- [`python3`](https://wiki.python.org/moin/BeginnersGuide)
Other requisites are:
- `urdf_parser_py`
- `jax`
- `casadi`
- `pytorch`
- `numpy`
They will be installed in the installation step!
## 💾 Installation
The installation can be done either using the Python provided by apt (on Debian-based distros) or via conda (on Linux and macOS).
### Installation with pip
Install `python3`, if not installed (in **Ubuntu 20.04**):
```bash
sudo apt install python3.8
```
Create a [virtual environment](https://docs.python.org/3/library/venv.html#venv-def), if you prefer. For example:
```bash
pip install virtualenv
python3 -m venv your_virtual_env
source your_virtual_env/bin/activate
```
Inside the virtual environment, install the library from pip:
- Install **Jax** interface:
```bash
pip install adam-robotics[jax]
```
- Install **CasADi** interface:
```bash
pip install adam-robotics[casadi]
```
- Install **PyTorch** interface:
```bash
pip install adam-robotics[pytorch]
```
- Install **ALL** interfaces:
```bash
pip install adam-robotics[all]
```
If you want the last version:
```bash
pip install adam-robotics[selected-interface]@git+https://github.com/ami-iit/ADAM
```
or clone the repo and install:
```bash
git clone https://github.com/ami-iit/ADAM.git
cd ADAM
pip install .[selected-interface]
```
### Installation with conda
Install in a conda environment the required dependencies:
- **Jax** interface dependencies:
```bash
mamba create -n adamenv -c conda-forge -c robostack jax numpy lxml prettytable matplotlib ros-noetic-urdfdom-py
```
- **CasADi** interface dependencies:
```bash
mamba create -n adamenv -c conda-forge -c robostack casadi numpy lxml prettytable matplotlib ros-noetic-urdfdom-py
```
- **PyTorch** interface dependencies:
```bash
mamba create -n adamenv -c conda-forge -c robostack pytorch numpy lxml prettytable matplotlib ros-noetic-urdfdom-py
```
- **ALL** interfaces dependencies:
```bash
mamba create -n adamenv -c conda-forge -c robostack jax casadi pytorch numpy lxml prettytable matplotlib ros-noetic-urdfdom-py
```
Activate the environment, clone the repo and install the library:
```bash
mamba activate adamenv
git clone https://github.com/dic-iit/ADAM.git
cd ADAM
pip install --no-deps .
```
## 🚀 Usage
The following are small snippets of the use of ADAM. More examples are arriving!
Have also a look at te `tests` folder.
### Jax interface
```python
from adam.jax import KinDynComputations
import icub_models
import numpy as np
# if you want to icub-models https://github.com/robotology/icub-models to retrieve the urdf
model_path = icub_models.get_model_file("iCubGazeboV2_5")
# The joint list
joints_name_list = [
'torso_pitch', 'torso_roll', 'torso_yaw', 'l_shoulder_pitch',
'l_shoulder_roll', 'l_shoulder_yaw', 'l_elbow', 'r_shoulder_pitch',
'r_shoulder_roll', 'r_shoulder_yaw', 'r_elbow', 'l_hip_pitch', 'l_hip_roll',
'l_hip_yaw', 'l_knee', 'l_ankle_pitch', 'l_ankle_roll', 'r_hip_pitch',
'r_hip_roll', 'r_hip_yaw', 'r_knee', 'r_ankle_pitch', 'r_ankle_roll'
]
# Specify the root link
root_link = 'root_link'
kinDyn = KinDynComputations(model_path, joints_name_list, root_link)
w_H_b = np.eye(4)
joints = np.ones(len(joints_name_list))
M = kinDyn.mass_matrix(w_H_b, joints)
print(M)
```
### CasADi interface
```python
from adam.casadi import KinDynComputations
import icub_models
import numpy as np
# if you want to icub-models https://github.com/robotology/icub-models to retrieve the urdf
model_path = icub_models.get_model_file("iCubGazeboV2_5")
# The joint list
joints_name_list = [
'torso_pitch', 'torso_roll', 'torso_yaw', 'l_shoulder_pitch',
'l_shoulder_roll', 'l_shoulder_yaw', 'l_elbow', 'r_shoulder_pitch',
'r_shoulder_roll', 'r_shoulder_yaw', 'r_elbow', 'l_hip_pitch', 'l_hip_roll',
'l_hip_yaw', 'l_knee', 'l_ankle_pitch', 'l_ankle_roll', 'r_hip_pitch',
'r_hip_roll', 'r_hip_yaw', 'r_knee', 'r_ankle_pitch', 'r_ankle_roll'
]
# Specify the root link
root_link = 'root_link'
kinDyn = KinDynComputations(model_path, joints_name_list, root_link)
w_H_b = np.eye(4)
joints = np.ones(len(joints_name_list))
M = kinDyn.mass_matrix_fun()
print(M(w_H_b, joints))
```
### PyTorch interface
```python
from adam.pytorch import KinDynComputations
import icub_models
import numpy as np
# if you want to icub-models https://github.com/robotology/icub-models to retrieve the urdf
model_path = icub_models.get_model_file("iCubGazeboV2_5")
# The joint list
joints_name_list = [
'torso_pitch', 'torso_roll', 'torso_yaw', 'l_shoulder_pitch',
'l_shoulder_roll', 'l_shoulder_yaw', 'l_elbow', 'r_shoulder_pitch',
'r_shoulder_roll', 'r_shoulder_yaw', 'r_elbow', 'l_hip_pitch', 'l_hip_roll',
'l_hip_yaw', 'l_knee', 'l_ankle_pitch', 'l_ankle_roll', 'r_hip_pitch',
'r_hip_roll', 'r_hip_yaw', 'r_knee', 'r_ankle_pitch', 'r_ankle_roll'
]
# Specify the root link
root_link = 'root_link'
kinDyn = KinDynComputations(model_path, joints_name_list, root_link)
w_H_b = np.eye(4)
joints = np.ones(len(joints_name_list))
M = kinDyn.mass_matrix(w_H_b, joints)
print(M)
```
## 🦸♂️ Contributing
**ADAM** is an open-source project. Contributions are very welcome!
Open an issue with your feature request or if you spot a bug. Then, you can also proceed with a Pull-requests! :rocket:
## Todo
- [x] Center of Mass position
- [x] Jacobians
- [x] Forward kinematics
- [x] Mass Matrix via CRBA
- [x] Centroidal Momentum Matrix via CRBA
- [x] Recursive Newton-Euler algorithm (still no acceleration in the algorithm, since it is used only for the computation of the bias force)
- [ ] Articulated Body algorithm
نیازمندی
| مقدار | نام |
|---|---|
| >=1.20 | numpy |
| - | scipy |
| - | casadi |
| - | prettytable |
| - | urdf-parser-py |
| - | jax |
| - | jaxlib |
| - | casadi |
| - | torch |
| - | casadi |
| - | jax |
| - | jaxlib |
| - | torch |
| - | jax |
| - | jaxlib |
| - | casadi |
| - | torch |
| - | pytest |
| - | idyntree |
| - | gym-ignition-models |
| - | black |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl adam-robotics-0.0.6:
pip install adam-robotics-0.0.6.whl
نصب پکیج tar.gz adam-robotics-0.0.6:
pip install adam-robotics-0.0.6.tar.gz