🧭 IMU

The IMU sensor models an Inertial Measurement Unit attached to a rigid link. It returns linear acceleration, angular velocity, and magnetic field as a NamedTuple (lin_acc, ang_vel, mag), with optional misalignment, noise, drift, delay, and jitter to mimic real hardware.

The full example script is at examples/sensors/imu_franka.py.

Scene setup

import genesis as gs
import numpy as np

gs.init(backend=gs.gpu)

scene = gs.Scene(
    viewer_options=gs.options.ViewerOptions(
        camera_pos=(3.5, 0.0, 2.5),
        camera_lookat=(0.0, 0.0, 0.5),
        camera_fov=40,
    ),
    sim_options=gs.options.SimOptions(dt=0.01),
    show_viewer=True,
)

scene.add_entity(gs.morphs.Plane())
franka = scene.add_entity(gs.morphs.MJCF(file="xml/franka_emika_panda/panda.xml"))
end_effector = franka.get_link("hand")
motors_dof = (0, 1, 2, 3, 4, 5, 6)

Adding the IMU sensor

Attach the IMU onto the entity at the end effector by specifying entity_idx and link_idx_local:

imu = scene.add_sensor(
    gs.sensors.IMU(
        entity_idx=franka.idx,
        link_idx_local=end_effector.idx_local,
        pos_offset=(0.0, 0.0, 0.15),
        # sensor characteristics
        acc_cross_axis_coupling=(0.0, 0.01, 0.02),
        gyro_cross_axis_coupling=(0.03, 0.04, 0.05),
        acc_noise=(0.01, 0.01, 0.01),
        gyro_noise=(0.01, 0.01, 0.01),
        acc_random_walk=(0.001, 0.001, 0.001),
        gyro_random_walk=(0.001, 0.001, 0.001),
        delay=0.01,
        jitter=0.01,
        draw_debug=True,
    )
)

The IMU constructor exposes:

• pos_offset - sensor position relative to the link frame.

• acc_cross_axis_coupling / gyro_cross_axis_coupling - sensor misalignment.

• acc_noise / gyro_noise - Gaussian noise per axis.

• acc_random_walk / gyro_random_walk - gradual drift over time.

• delay / jitter - timing realism.

• draw_debug - visualize the sensor frame in the viewer.

Motion control and simulation

scene.build()

franka.set_dofs_kp(np.array([4500, 4500, 3500, 3500, 2000, 2000, 2000, 100, 100]))
franka.set_dofs_kv(np.array([450, 450, 350, 350, 200, 200, 200, 10, 10]))

circle_center = np.array([0.4, 0.0, 0.5])
circle_radius = 0.15
rate = np.deg2rad(2.0)

def control_franka_circle_path(i):
    pos = circle_center + np.array([np.cos(i * rate), np.sin(i * rate), 0]) * circle_radius
    qpos = franka.inverse_kinematics(
        link=end_effector,
        pos=pos,
        quat=np.array([0, 1, 0, 0]),
    )
    franka.control_dofs_position(qpos[:-2], motors_dof)
    scene.draw_debug_sphere(pos, radius=0.01, color=(1.0, 0.0, 0.0, 0.5))

for i in range(1000):
    scene.step()
    control_franka_circle_path(i)

The robot traces a horizontal circle while maintaining a fixed orientation. The circular motion creates centripetal acceleration that the IMU detects, along with gravitational effects based on the sensor’s orientation.

After the build, both measured and ground truth IMU data are available:

print("Ground truth data:")
print(imu.read_ground_truth())
print("Measured data:")
print(imu.read())

The IMU returns data as a NamedTuple with fields:

• lin_acc - linear acceleration in m/s² (3D vector)

• ang_vel - angular velocity in rad/s (3D vector)

• mag - magnetic field in Tesla (3D vector)