PyTorch batched usage

The following example shows how to call an instance of the adam.pytorch.KinDynComputationsBatch class and use it to compute the mass matrix and forward dynamics of a floating-base robot.

Note

The first time you run a function from this module, it will take a bit longer to execute as they are being compiled by JAX.

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)