DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories

TL;DR

This paper presents a deep reinforcement learning framework for controlling floating platforms, enabling precise maneuvers in uncertain environments, with applications in space simulation and autonomous navigation testing.

Contribution

It introduces a novel DRL-based suite for floating platform control that is robust, adaptable, and transferable from simulation to real-world environments.

Findings

Achieves robustness and adaptability in platform control

Enables transfer from simulation to real-world environments

Provides fast training and large-scale testing capabilities

Abstract

This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments. Floating platforms serve as versatile test-beds to emulate micro-gravity environments on Earth, useful to test autonomous navigation systems for space applications. Our approach addresses the system and environmental uncertainties in controlling such platforms by training policies capable of precise maneuvers amid dynamic and unpredictable conditions. Leveraging Deep Reinforcement Learning (DRL) techniques, our suite achieves robustness, adaptability, and good transferability from simulation to reality. Our deep reinforcement learning framework provides advantages such as fast training times, large-scale testing capabilities, rich visualization options, and ROS bindings for integration with real-world robotic systems. Being open access, our suite serves as a comprehensive platform for practitioners who want to replicate similar research in their own simulated environments and labs.