Article

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

A research team completed an on-orbit demonstration on the International Space Station's Astrobee free-flying robot of machine learning-based warm starts to accelerate trajectory optimization, using a neural network to predict initial values for a sequential convex programming (SCP) solver, which then enforces safety constraints. In scenarios including rotational dynamics, the trained network reduced the number of solver iterations required for convergence by 60%, and by 50% in obstacle scenarios drawn from the training distribution. The work was published at iSpaRo 2025.

Part of the event: Machine Learning Warm-Start Accelerated Trajectory Optimization Completes On-Orbit Demonstration

This page provides a summary and source link only.