  Reliable and efficient trajectory optimization methods are a fundamental need
for autonomous dynamical systems, effectively enabling applications including
rocket landing, hypersonic reentry, spacecraft rendezvous, and docking. Within
such safety-critical application areas, the complexity of the emerging
trajectory optimization problems has motivated the application of AI-based
techniques to enhance the performance of traditional approaches. However,
current AI-based methods either attempt to fully replace traditional control
algorithms, thus lacking constraint satisfaction guarantees and incurring in
expensive simulation, or aim to solely imitate the behavior of traditional
methods via supervised learning. To address these limitations, this paper
proposes the Autonomous Rendezvous Transformer (ART) and assesses the
capability of modern generative models to solve complex trajectory optimization
problems, both from a forecasting and control standpoint. Specifically, this
work assesses the capabilities of Transformers to (i) learn near-optimal
policies from previously collected data, and (ii) warm-start a sequential
optimizer for the solution of non-convex optimal control problems, thus
guaranteeing hard constraint satisfaction. From a forecasting perspective,
results highlight how ART outperforms other learning-based architectures at
predicting known fuel-optimal trajectories. From a control perspective,
empirical analyses show how policies learned through Transformers are able to
generate near-optimal warm-starts, achieving trajectories that are (i) more
fuel-efficient, (ii) obtained in fewer sequential optimizer iterations, and
(iii) computed with an overall runtime comparable to benchmarks based on convex
optimization.
