Event
Machine Learning Warm-Start Accelerated Trajectory Optimization Completes On-Orbit Demonstration
1 reports · 1 sources
The research team completed an on-orbit demonstration of machine learning warm-start accelerated trajectory optimization aboard the Astrobee free-flying robot on the International Space Station: a neural network predicts the initial values for the sequential convex programming (SCP) solver, and then the SCP solver guarantees the safety constraints. The team states that the trained network reduced the number of iterations required for convergence by 60% in scenarios involving rotational dynamics, and by 50% in obstacle scenarios drawn from the warm-start model's training distribution. This work was published at iSpaRo 2025.
Report timeline
- 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.
arXiv cs.RO ·