# Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System

arXiv: https://arxiv.org/abs/2201.04180 · HTML: https://mlsanity.com/paper/2201.04180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Neuroevolution in Games: State of the Art and Open Challenges](https://mlsanity.com/paper/1410.7326/) (2014)
- [A Closer Look at Deep Policy Gradients](https://mlsanity.com/paper/1811.02553/) (2018)
- [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, 3 of 3)

- [Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning](https://mlsanity.com/paper/2308.09075/) (2023)
- [Learning-Aided Control of Robotic Tether-Net with Maneuverable Nodes to Capture Large Space Debris](https://mlsanity.com/paper/2403.07125/) (2024)
- [Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations](https://mlsanity.com/paper/2606.07580/) (2026)

## Abstract (arXiv)

Tether-net launched from a chaser spacecraft provides a promising method to capture and dispose of large space debris in orbit. This tether-net system is subject to several sources of uncertainty in sensing and actuation that affect the performance of its net launch and closing control. Earlier reliability-based optimization approaches to design control actions however remain challenging and computationally prohibitive to generalize over varying launch scenarios and target (debris) state relative to the chaser. To search for a general and reliable control policy, this paper presents a reinforcement learning framework that integrates a proximal policy optimization (PPO2) approach with net dynamics simulations. The latter allows evaluating the episodes of net-based target capture, and estimate the capture quality index that serves as the reward feedback to PPO2. Here, the learned policy is designed to model the timing of the net closing action based on the state of the moving net and the target, under any given launch scenario. A stochastic state transition model is considered in order to incorporate synthetic uncertainties in state estimation and launch actuation. Along with notable reward improvement during training, the trained policy demonstrates capture performance (over a wide range of launch/target scenarios) that is close to that obtained with reliability-based optimization run over an individual scenario.