Reconfigurable Manipulator Simulation for Robotics and Multimodal Machine Learning Application: Aaria
This provides a tool for generating synthetic training data for robotics and multimodal machine learning applications, though it is incremental as it builds on existing simulation frameworks.
The paper introduces Aaria, a Simulink SimMechanics-based simulation model for serial manipulators with 1-6 DOF that generates kinematic and dynamic data, including joint positions/velocities/torques and IMU outputs, to create multimodal datasets for machine learning in robotics.
This paper represents a systematic way for generation of Aaria, a simulated model for serial manipulators for the purpose of kinematic or dynamic analysis with a vast variety of structures based on Simulink SimMechanics. The proposed model can receive configuration parameters, for instance in accordance with modified Denavit-Hartenberg convention, or trajectories for its base or joints for structures with 1 to 6 degrees of freedom (DOF). The manipulator is equipped with artificial joint sensors as well as simulated Inertial Measurement Units (IMUs) on each link. The simulation output can be positions, velocities, torques, in the joint space or IMU outputs; angular velocity, linear acceleration, tool coordinates with respect to the inertial frame. This simulation model is a source of a dataset for virtual multimodal sensory data for automation of robot modeling and control designed for machine learning and deep learning approaches based on big data.