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https://github.com/ami-iit/adam
adam implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.
https://github.com/ami-iit/adam
adam-robotics automatic-differentiation casadi jax motion-planning numpy optimization python pytorch reinforcement-learning rigid-body-dynamics robotics urdf
Last synced: about 19 hours ago
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adam implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.
- Host: GitHub
- URL: https://github.com/ami-iit/adam
- Owner: ami-iit
- License: bsd-3-clause
- Created: 2021-05-25T07:55:26.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-26T10:36:24.000Z (about 2 months ago)
- Last Synced: 2024-10-29T19:59:46.387Z (about 2 months ago)
- Topics: adam-robotics, automatic-differentiation, casadi, jax, motion-planning, numpy, optimization, python, pytorch, reinforcement-learning, rigid-body-dynamics, robotics, urdf
- Language: Python
- Homepage: https://adam-docs.readthedocs.io/en/latest/
- Size: 1.86 MB
- Stars: 131
- Watchers: 14
- Forks: 20
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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_ and _body fixed representations_ (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.
### Table of contents
- [🐍 Dependencies](#-dependencies)
- [💾 Installation](#-installation)
- [🐍 Installation with pip](#-installation-with-pip)
- [📦 Installation with conda](#-installation-with-conda)
- [Installation from conda-forge package](#installation-from-conda-forge-package)
- [🔨 Installation from repo](#-installation-from-repo)
- [🚀 Usage](#-usage)
- [Jax interface](#jax-interface)
- [CasADi interface](#casadi-interface)
- [PyTorch interface](#pytorch-interface)
- [PyTorch Batched interface](#pytorch-batched-interface)
- [🦸♂️ Contributing](#️-contributing)
- [Todo](#todo)
## 🐍 Dependencies
- [`python3`](https://wiki.python.org/moin/BeginnersGuide)
Other requisites are:
- `urdf_parser_py`
- `jax`
- `casadi`
- `pytorch`
- `numpy`
- `jax2torch`
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
#### Installation from conda-forge package
- Install **CasADi** interface:
```bash
mamba create -n adamenv -c conda-forge adam-robotics-casadi
```
- Install **Jax** interface (warning: not available on Windows):
```bash
mamba create -n adamenv -c conda-forge adam-robotics-jax
```
- Install **PyTorch** interface (warning: not available on Windows):
```bash
mamba create -n adamenv -c conda-forge adam-robotics-pytorch
```
- Install **ALL** interfaces (warning: not available on Windows):
```bash
mamba create -n adamenv -c conda-forge adam-robotics-all
```
> [!NOTE]
> Check also the conda JAX installation guide [here](https://jax.readthedocs.io/en/latest/installation.html#conda-community-supported)
### 🔨 Installation from repo
Install in a conda environment the required dependencies:
- **Jax** interface dependencies:
```bash
mamba create -n adamenv -c conda-forge jax numpy lxml prettytable matplotlib urdfdom-py
```
- **CasADi** interface dependencies:
```bash
mamba create -n adamenv -c conda-forge casadi numpy lxml prettytable matplotlib urdfdom-py
```
- **PyTorch** interface dependencies:
```bash
mamba create -n adamenv -c conda-forge pytorch numpy lxml prettytable matplotlib urdfdom-py jax2torch
```
- **ALL** interfaces dependencies:
```bash
mamba create -n adamenv -c conda-forge jax casadi pytorch numpy lxml prettytable matplotlib urdfdom-py jax2torch
```
Activate the environment, clone the repo and install the library:
```bash
mamba activate adamenv
git clone https://github.com/ami-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 the `tests` folder.
### Jax interface
> [!NOTE]
> Check also the Jax installation guide [here](https://jax.readthedocs.io/en/latest/installation.html#)
```python
import adam
from adam.jax import KinDynComputations
import icub_models
import numpy as np
import jax.numpy as jnp
from jax import jit, vmap
# 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'
]
kinDyn = KinDynComputations(model_path, joints_name_list)
# choose the representation, if you want to use the body fixed representation
kinDyn.set_frame_velocity_representation(adam.Representations.BODY_FIXED_REPRESENTATION)
# or, if you want to use the mixed representation (that is the default)
kinDyn.set_frame_velocity_representation(adam.Representations.MIXED_REPRESENTATION)
w_H_b = np.eye(4)
joints = np.ones(len(joints_name_list))
M = kinDyn.mass_matrix(w_H_b, joints)
print(M)
w_H_f = kinDyn.forward_kinematics('frame_name', w_H_b, joints)
# IMPORTANT! The Jax Interface function execution can be slow! We suggest to jit them.
# For example:
def frame_forward_kinematics(w_H_b, joints):
# This is needed since str is not a valid JAX type
return kinDyn.forward_kinematics('frame_name', w_H_b, joints)
jitted_frame_fk = jit(frame_forward_kinematics)
w_H_f = jitted_frame_fk(w_H_b, joints)
# In the same way, the functions can be also vmapped
vmapped_frame_fk = vmap(frame_forward_kinematics, in_axes=(0, 0))
# which can be also jitted
jitted_vmapped_frame_fk = jit(vmapped_frame_fk)
# and called on a batch of data
joints_batch = jnp.tile(joints, (1024, 1))
w_H_b_batch = jnp.tile(w_H_b, (1024, 1, 1))
w_H_f_batch = jitted_vmapped_frame_fk(w_H_b_batch, joints_batch)
```
> [!NOTE]
> The first call of the jitted function can be slow, since JAX needs to compile the function. Then it will be faster!
### CasADi interface
```python
import casadi as cs
import adam
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'
]
kinDyn = KinDynComputations(model_path, joints_name_list)
# choose the representation you want to use the body fixed representation
kinDyn.set_frame_velocity_representation(adam.Representations.BODY_FIXED_REPRESENTATION)
# or, if you want to use the mixed representation (that is the default)
kinDyn.set_frame_velocity_representation(adam.Representations.MIXED_REPRESENTATION)
w_H_b = np.eye(4)
joints = np.ones(len(joints_name_list))
M = kinDyn.mass_matrix_fun()
print(M(w_H_b, joints))
# If you want to use the symbolic version
w_H_b = cs.SX.eye(4)
joints = cs.SX.sym('joints', len(joints_name_list))
M = kinDyn.mass_matrix_fun()
print(M(w_H_b, joints))
# This is usable also with casadi.MX
w_H_b = cs.MX.eye(4)
joints = cs.MX.sym('joints', len(joints_name_list))
M = kinDyn.mass_matrix_fun()
print(M(w_H_b, joints))
```
### PyTorch interface
```python
import adam
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'
]
kinDyn = KinDynComputations(model_path, joints_name_list)
# choose the representation you want to use the body fixed representation
kinDyn.set_frame_velocity_representation(adam.Representations.BODY_FIXED_REPRESENTATION)
# or, if you want to use the mixed representation (that is the default)
kinDyn.set_frame_velocity_representation(adam.Representations.MIXED_REPRESENTATION)
w_H_b = np.eye(4)
joints = np.ones(len(joints_name_list))
M = kinDyn.mass_matrix(w_H_b, joints)
print(M)
```
### PyTorch Batched interface
> [!NOTE]
> When using this interface, note that the first call of the jitted function can be slow, since JAX needs to compile the function. Then it will be faster!
```python
import adam
from adam.pytorch import KinDynComputationsBatch
import icub_models
# if you want to icub-models
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'
]
kinDyn = KinDynComputationsBatch(model_path, joints_name_list)
# choose the representation you want to use the body fixed representation
kinDyn.set_frame_velocity_representation(adam.Representations.BODY_FIXED_REPRESENTATION)
# or, if you want to use the mixed representation (that is the default)
kinDyn.set_frame_velocity_representation(adam.Representations.MIXED_REPRESENTATION)
w_H_b = np.eye(4)
joints = np.ones(len(joints_name_list))
num_samples = 1024
w_H_b_batch = torch.tensor(np.tile(w_H_b, (num_samples, 1, 1)), dtype=torch.float32)
joints_batch = torch.tensor(np.tile(joints, (num_samples, 1)), dtype=torch.float32)
M = kinDyn.mass_matrix(w_H_b_batch, joints_batch)
w_H_f = kinDyn.forward_kinematics('frame_name', w_H_b_batch, joints_batch)
```
## 🦸♂️ 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:
> [!WARNING]
> REPOSITORY UNDER DEVELOPMENT! We cannot guarantee stable API
## 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