The US Naval Research Laboratory (NRL) has announced the successful test of reinforcement-learning (RL)-based autonomous robotic flight in space, using an ‘Astrobee’ zero-gravity robot stationed aboard the International Space Station. Simulated Zero Gravity RobotsIn most space robotic applications, a controller uses teleoperation—the remote control of a mechanical device—to command and control the robot’s movements. For their zero-gravity robot application, the team used the Proximal Policy Optimization algorithm, a method of deep reinforcement learning. For example, the team initially tasked the simulated Astrobee zero-gravity robot with moving to a single, fixed position in space. However, when the video feed resumed, they saw that the zero-gravity robot had successfully completed its five-minute mission and returned to its docking station.