adam-robotics 0.0.3

Automatic Differentiation for rigid body AlgorithMs

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:

• Jax
• CasADi
• PyTorch
• NumPy

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.

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⚠️ REPOSITORY UNDER DEVELOPMENT ⚠️
We cannot guarantee stable API

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🐍 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 Debian-based distros) or via conda (on Linux and macOS).

Installation with pip

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]

Installation with conda

Install in a conda environment the required dependencies:

• Jax interface dependencies:

  mamba create -n adamenv -c conda-forge -c robostack casadi numpy lxml prettytable matplotlib ros-noetic-urdfdom-py
  

• CasADi interface dependencies:

  mamba create -n adamenv -c conda-forge -c robostack jax 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 .

🚀 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

from adam.jax 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 interface

from adam.casadi 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 interface

from adam.pytorch 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! :rocket:

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