# Learning-Aided Control of Robotic Tether-Net with Maneuverable Nodes to Capture Large Space Debris

arXiv: https://arxiv.org/abs/2403.07125 · HTML: https://mlsanity.com/paper/2403.07125/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System](https://mlsanity.com/paper/2201.04180/) (2022)
- [Learning Constrained Corner Node Trajectories of a Tether Net System for Space Debris Capture](https://mlsanity.com/paper/2307.03061/) (2023)
- [Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents](https://mlsanity.com/paper/1903.07107/) (2019)

## Built on by (most cited first, 2 of 2)

- [Designing Active Tether-Net Systems for Space Debris Capture with Graph-Learning-Aided Mixed-Combinatorial Optimization](https://mlsanity.com/paper/2605.29021/) (2026)
- [Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations](https://mlsanity.com/paper/2606.07580/) (2026)

## Abstract (arXiv)

Maneuverable tether-net systems launched from an unmanned spacecraft offer a promising solution for the active removal of large space debris. Guaranteeing the successful capture of such space debris is dependent on the ability to reliably maneuver the tether-net system -- a flexible, many-DoF (thus complex) system -- for a wide range of launch scenarios. Here, scenarios are defined by the relative location of the debris with respect to the chaser spacecraft. This paper represents and solves this problem as a hierarchically decentralized implementation of robotic trajectory planning and control and demonstrates the effectiveness of the approach when applied to two different tether-net systems, with 4 and 8 maneuverable units (MUs), respectively. Reinforcement learning (policy gradient) is used to design the centralized trajectory planner that, based on the relative location of the target debris at the launch of the net, computes the final aiming positions of each MU, from which their trajectory can be derived. Each MU then seeks to follow its assigned trajectory by using a decentralized PID controller that outputs the MU's thrust vector and is informed by noisy sensor feedback (for realism) of its relative location. System performance is assessed in terms of capture success and overall fuel consumption by the MUs. Reward shaping and surrogate models are used to respectively guide and speed up the RL process. Simulation-based experiments show that this approach allows the successful capture of debris at fuel costs that are notably lower than nominal baselines, including in scenarios where the debris is significantly off-centered compared to the approaching chaser spacecraft.