Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators

2026-06-15

AI Summary

This study addresses the challenge of real-time trajectory generation for on-orbit space manipulators approaching tumbling targets, where strong nonlinear coupling severely complicates control. The terminal approach is decomposed into two phases: center-of-mass translational planning and a subsequent attitude–manipulator torque allocation stage, with particular focus on the latter as the computational bottleneck. To accelerate this phase, the work innovatively introduces a causal Transformer as a warm-start strategy for sequential convex programming (SCP), integrating action chunking encoding and flow-matching decoding to simultaneously preserve trajectory optimality and significantly enhance computational efficiency and robustness. Experimental results across 300 test scenarios demonstrate up to a 28% reduction in SCP iterations and a 23% decrease in runtime; when applied to nonconvex feasibility projection, the method nearly halves computation time and effectively suppresses high-cost anomalous behaviors induced by heuristic initial guesses.

Abstract

Real-time trajectory generation for on-orbit robotic servicing is challenging due to the nonlinear coupling between spacecraft bus motion, manipulator dynamics, visibility cone, and trajectory-level safety constraints. This paper studies learning-based warm-starting for sequential convex programming (SCP) in the terminal approach of a space manipulator toward a tumbling target. The proposed framework decomposes the problem into a system center-of-mass translational planning stage and a coupled attitude--manipulator torque-allocation stage, and applies a causal transformer warm-start to the latter, which constitutes the dominant computational bottleneck. Linear and flow matching action decoders are compared under different action-chunking and training dataset sizes, and the resulting warm-starts are evaluated under both cost-optimal and feasibility projection using SCP. Across 300 held-out scenarios, the learned warm-start reduces the second-stage SCP iteration count by up to 28% and the runtime by 23% while preserving the final control-cost distribution. When the learned warm-starts are used for nonconvex feasibility projection, they nearly halve the runtime relative to cost-optimal SCP, while avoiding the catastrophic high-cost tail behavior observed when initialized heuristically. These results indicate that sequence-model warm-starts can improve both the computational efficiency and trajectory robustness of optimization-based terminal guidance for space manipulation.

Problem

• space manipulators
• tumbling objects
• real-time trajectory generation
• nonlinear coupling
• terminal approach

Innovation

• causal transformer
• warm-starting
• sequential convex programming
• space manipulator
• trajectory optimization