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) using:
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.
We cannot guarantee stable API
Other requisites are:
urdf_parser_py
jax
casadi
pytorch
numpy
They will be installed in the installation step!
The installation can be done either using the Python provided by apt (on Debian-based distros) or via conda (on Linux and macOS).
Install python3
, if not installed (in Ubuntu 20.04):
sudo apt install python3.8
Create a virtual environment, if you prefer. For example:
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:
pip install adam-robotics[jax]
-
Install CasADi interface:
pip install adam-robotics[casadi]
-
Install PyTorch interface:
pip install adam-robotics[pytorch]
-
Install ALL interfaces:
pip install adam-robotics[all]
If you want the last version:
pip install adam-robotics[selected-interface]@git+https://github.com/ami-iit/ADAM
or clone the repo and install:
git clone https://github.com/ami-iit/ADAM.git
cd ADAM
pip install .[selected-interface]
Install in a conda environment the required dependencies:
-
Jax interface dependencies:
mamba create -n adamenv -c conda-forge -c robostack jax numpy lxml prettytable matplotlib ros-noetic-urdfdom-py
-
CasADi interface dependencies:
mamba create -n adamenv -c conda-forge -c robostack casadi numpy lxml prettytable matplotlib ros-noetic-urdfdom-py
-
PyTorch interface dependencies:
mamba create -n adamenv -c conda-forge -c robostack pytorch numpy lxml prettytable matplotlib ros-noetic-urdfdom-py
-
ALL interfaces dependencies:
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:
mamba activate adamenv
git clone https://github.com/dic-iit/ADAM.git
cd ADAM
pip install --no-deps .
The following are small snippets of the use of ADAM. More examples are arriving!
Have also a look at te tests
folder.
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)
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))
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)
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! 🚀
- Center of Mass position
- Jacobians
- Forward kinematics
- Mass Matrix via CRBA
- Centroidal Momentum Matrix via CRBA
- 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