# PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment

arXiv: https://arxiv.org/abs/2407.03224 · HTML: https://mlsanity.com/paper/2407.03224/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learning](https://mlsanity.com/paper/2008.03215/) (2020)
- [Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay](https://mlsanity.com/paper/2402.18487/) (2024)
- [Mobility Strategy of Multi-Limbed Climbing Robots for Asteroid Exploration](https://mlsanity.com/paper/2306.07688/) (2023)

## Built on by (most cited first, 4 of 4)

- [Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05996/) (2025)
- [MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation](https://mlsanity.com/paper/2509.21045/) (2025)
- [Crossing the Sim2Real Gap Between Simulation and Ground Testing to Space Deployment of Autonomous Free-flyer Control](https://mlsanity.com/paper/2512.03736/) (2025)
- [Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training](https://mlsanity.com/paper/2604.26833/) (2026)

## Abstract (arXiv)

In the field of space exploration, floating platforms play a crucial role in scientific investigations and technological advancements. However, controlling these platforms in zero-gravity environments presents unique challenges, including uncertainties and disturbances. This paper introduces an innovative approach that combines Proximal Policy Optimization (PPO) with Model Predictive Control (MPC) in the zero-gravity laboratory (Zero-G Lab) at the University of Luxembourg. This approach leverages PPO's reinforcement learning power and MPC's precision to navigate the complex control dynamics of floating platforms. Unlike traditional control methods, this PPO-MPC approach learns from MPC predictions, adapting to unmodeled dynamics and disturbances, resulting in a resilient control framework tailored to the zero-gravity environment. Simulations and experiments in the Zero-G Lab validate this approach, showcasing the adaptability of the PPO agent. This research opens new possibilities for controlling floating platforms in zero-gravity settings, promising advancements in space exploration.