Control Engine

QuantumLoco Kinematics v4

QuantumLoco Kinematics v4 is an advanced neural controller framework for multi-joint bipedal and quadrupedal systems. Built on real-time neural predictive optimization, it guarantees microsecond-level joint torque distribution under dynamic physical disturbances.

Request Integration SDK ➔

QuantumLoco Controller Kinematic Trajectory Visualization

Performance & Hardware Specs

Inference Latency

1.45 ms

DOFs Supported

Up to 48 Actuators

Simulation Sync Rate

1000 Hz

Architecture Support

ARM64 / x86_64 Edge

Detailed System Overview

Deep reinforcement learning locomotion engine featuring adaptive terrain feedback, active disturbance rejection, and ultra-low latency torque computation on edge microprocessors.

Integration Code Snippet

Deploy directly onto Linux-based ROS 2 / ROS 3 neural compute blocks using python or C++ bindings.

import csj_quantum_loco as ql

# Initialize hardware bus & neural policy
robot_model = ql.BipedalRig(urdf_path="./assets/humanoid_v4.urdf")
policy = ql.NeuralKinematicsPolicy.load_pretrained("quantum_loco_v4.bin")

# Main 1000Hz Control Loop
while robot_model.is_active():
    sensors = robot_model.read_imu_and_encoders()
    optimal_torques = policy.compute_torque(sensors, target_velocity=(1.2, 0.0, 0.0))
    robot_model.apply_actuator_commands(optimal_torques)