A Modular Simulation Platform for Training Robots via Deep Reinforcement Learning and Multibody Dynamics

Abstract

In this work we focus on the role of Multibody Simulation in creating Reinforcement Learning virtual environments for robotic manipulation, showing a versatile, efficient and open source toolchain to create directly from CAD models. Using the Chrono::Solidworks plugin we are able to create robotic environments in the 3D CAD software Solidworks® and later convert them into PyChrono models (PyChrono is an open source Python module for multibody simulation). In addition, we demonstrate how collision detection can be made more efficient by introducing a limited number of contact primitives instead of performing collision detection and evaluation on complex 3D meshes, still reaching a policy able to avoid unwanted collisions. We tested this approach on a 6DOF robot Comau Racer3: the robot, together with a 2 fingers gripper (Hand-E by Robotiq) was modelled using Solidworks®, imported as a PyChrono model and then a NN was trained in simulation to control its motor torques to reach a target position. To demonstrate the versatility of this toolchain we also repeated the same procedure to model and then train the ABB IRB 120 robotic arm.