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.

翻译：在轨服务中的实时轨迹生成面临诸多挑战，这是由于航天器平台运动、机械臂动力学、可视锥约束以及轨迹级安全约束之间存在非线性耦合。本文研究了基于学习的序列凸规划（SCP）热启动方法，用于空间机械臂对翻滚目标的末端逼近。所提出的框架将问题分解为系统质心平动规划阶段与耦合的姿态-机械臂力矩分配阶段，并对构成主要计算瓶颈的后一阶段采用因果Transformer热启动。在划分不同动作块长度与训练数据集规模下，比较了线性动作解码器与流匹配动作解码器的性能，并通过基于SCP的成本最优投影与可行性投影评估热启动效果。在300个保留测试场景中，学习型热启动使第二阶段SCP迭代次数最多减少28%，运行时间降低23%，同时保持最终控制成本分布不变。当使用学习型热启动进行非凸可行性投影时，相对于成本最优SCP，其运行时间几乎减半，同时避免了启发式初始化中观察到的高成本极端尾部分布。这些结果表明，序列模型热启动能够提升基于优化的空间操控末端制导的计算效率与轨迹鲁棒性。