NumPy Backend

NumPy Backend#

The NumPy backend is the simplest choice for direct computation. Use it for model validation, quick experiments, and debugging.

Basic Usage#

import numpy as np
import adam
from adam.numpy import KinDynComputations
import icub_models

# Load model
model_path = icub_models.get_model_file("iCubGazeboV2_5")
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'
]

# Create KinDynComputations instance
kinDyn = KinDynComputations(model_path, joints_name_list)
kinDyn.set_frame_velocity_representation(adam.Representations.MIXED_REPRESENTATION)

# Define state
w_H_b = np.eye(4)  # Base at origin
joints = np.ones(len(joints_name_list)) * 0.1  # All joints at 0.1 rad

# Compute dynamics
M = kinDyn.mass_matrix(w_H_b, joints)
print(f"Mass matrix shape: {M.shape}")
print(f"Mass matrix:\n{M}")

# Forward kinematics
w_H_f = kinDyn.forward_kinematics('l_sole', w_H_b, joints)
print(f"Foot transform:\n{w_H_f}")

# Jacobian
J = kinDyn.jacobian('l_sole', w_H_b, joints)
print(f"Jacobian shape: {J.shape}")

Common Operations#

Centroidal Momentum Matrix

Ag = kinDyn.centroidal_momentum_matrix(w_H_b, joints)
print(f"Centroidal momentum matrix shape: {Ag.shape}")

Center of Mass

com = kinDyn.CoM_position(w_H_b, joints)
print(f"CoM position: {com}")

J_com = kinDyn.CoM_jacobian(w_H_b, joints)
print(f"CoM Jacobian shape: {J_com.shape}")

Bias Forces (Coriolis + Gravity)

base_vel = np.zeros(6)
joints_vel = np.zeros(len(joints_name_list))

h = kinDyn.bias_force(w_H_b, joints, base_vel, joints_vel)
print(f"Bias force: {h}")

Loading from MuJoCo#

You can also load models directly from MuJoCo:

import mujoco
from robot_descriptions.loaders.mujoco import load_robot_description
from adam.numpy import KinDynComputations

# Load MuJoCo model
mj_model = load_robot_description("g1_mj_description")

# Create KinDynComputations from MuJoCo model
kinDyn = KinDynComputations.from_mujoco_model(mj_model)
kinDyn.set_frame_velocity_representation(adam.Representations.MIXED_REPRESENTATION)

# Use as normal
w_H_b = np.eye(4)
joints = np.zeros(kinDyn.NDoF)
M = kinDyn.mass_matrix(w_H_b, joints)

See MuJoCo Integration for detailed MuJoCo integration guide.

When to Use NumPy#

✅ Good for: - Debugging and understanding results - Code that doesn’t need gradients or batching - Educational examples

❌ Not ideal for: - Computing gradients (use JAX or PyTorch) - Symbolic formulations (use CasADi) - Large-scale optimization

Next steps: If you need gradients and batching, see JAX Backend and PyTorch Backend. For optimization, see CasADi Backend.

Contents