ADAM

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 framework to compute, if needed, gradients, Jacobian, Hessians of rigid-body dynamics quantities. This approach enable the design of optimal control and reinforcement learning strategies in robotics.

ADAM is based on Roy Featherstone's Rigid Body Dynamics Algorithms.

⚠️ REPOSITORY UNDER DEVELOPMENT ⚠️
We cannot guarantee stable API

🔨 Dependencies

python3

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 Linux) or via conda (on Linux and macOS).

Installation with apt

Install python3, if not installed (in Ubuntu 20.04):

sudo apt install python3.8

Clone the repo and install the library:

git clone https://github.com/dic-iit/ADAM.git
cd ADAM
pip install .

preferably in a virtual environment. For example:

pip install virtualenv
python3 -m venv your_virtual_env
source your_virtual_env/bin/activate

Installation with conda

Install in a conda environment the required 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 .

🚀 Usage

The following are small snippets of the use of ADAM. More examples are arriving! Have also a look at te tests folder.

Jax

from adam.jax.computations import KinDynComputations
import gym_ignition_models
import numpy as np

# if you want to use gym-ignition https://github.com/robotology/gym-ignition to retrieve the urdf
model_path = gym_ignition_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

from adam.casadi.computations import KinDynComputations
import gym_ignition_models
import numpy as np

# if you want to use gym-ignition https://github.com/robotology/gym-ignition to retrieve the urdf
model_path = gym_ignition_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

from adam.pytorch.computations import KinDynComputations
import gym_ignition_models
import numpy as np

# if you want to use gym-ignition https://github.com/robotology/gym-ignition to retrieve the urdf
model_path = gym_ignition_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! 🚀

Todo

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

ANABUR920/ADAM