Deep Learning Warm Starts for Trajectory Optimization on the International Space Station

TL;DR

This paper demonstrates the first in-space use of machine learning to provide warm starts for trajectory optimization, significantly reducing computation time for autonomous robot control on the ISS.

Contribution

It introduces a neural network trained to generate warm starts for trajectory optimization, enabling faster and safer autonomous control of space robots.

Findings

Reduced solver iterations by 60% for rotational dynamics

Decreased iterations by 50% for obstacle avoidance scenarios

First in-space demonstration of learning-based trajectory optimization

Abstract

Trajectory optimization is a cornerstone of modern robot autonomy, enabling systems to compute trajectories and controls in real-time while respecting safety and physical constraints. However, it has seen limited usage in spaceflight applications due to its heavy computational demands that exceed the capability of most flight computers. In this work, we provide results on the first in-space demonstration of using machine learning-based warm starts for accelerating trajectory optimization for the Astrobee free-flying robot onboard the International Space Station (ISS). We formulate a data-driven optimal control approach that trains a neural network to learn the structure of the trajectory generation problem being solved using sequential convex programming (SCP). Onboard, this trained neural network predicts solutions for the trajectory generation problem and relies on using the SCP solver to enforce safety constraints for the system. Our trained network reduces the number of solver iterations required for convergence in cases including rotational dynamics by 60% and in cases with obstacles drawn from the training distribution of the warm start model by 50%. This work represents a significant milestone in the use of learning-based control for spaceflight applications and a stepping stone for future advances in the use of machine learning for autonomous guidance, navigation, & control.