ROS2 Gazebo Machine Learning: how number of worlds/robots effect performance скачать в хорошем качестве

ROS2 Gazebo Machine Learning: how number of worlds/robots effect performance

IMPORTANT NOTE: Gazebo is an open-source simulation platform similar in purpose to Isaac Sim. Its architecture relies on CPU-based simulation and does not support GPU scaling. Without GPU acceleration, large-scale reinforcement learning training is impractical. This demonstration uses Gazebo with CPU computation for inspection purposes only, not for current reinforcement learning standards. Because reinforcement learning training requires large observation datasets, multiple simulation instances are often run in parallel. In this video, we investigate hardware utilization under different conditions in several simulation environments, each with multiple robots. The goal is to maximize the number of agents in the reinforcement learning training session. Modified Panda robot URDF model (from generic source) by Amitai Heinrich Bergmann. ROS2 launch script and training script by Amitai Heinrich Bergmann. Video concept by Amitai Heinrich Bergmann. Video animations, art and editing by Amitai Heinrich Bergmann. © Bergmann Simulations 2025.