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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 and body fixed representations (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.

Table of contents

🐍 Dependencies

Other requisites are:

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:

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

Installation from conda-forge package

mamba create -n adamenv -c conda-forge adam-robotics

If you want to use jax or pytorch, just install the corresponding package as well.

[!NOTE] Check also the conda JAX installation guide here

🔨 Installation from repo

Install in a conda environment the required dependencies:

Activate the environment, clone the repo and install the library:

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

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

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

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!

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