Future in-orbit servicing missions, such as spacecraft capture, repair, and assembly, demand robotic systems capable of autonomously computing dynamically feasible, constrained trajectories in real time. Sequential Convex Programming (SCP) has emerged as an effective method for online trajectory optimization in these resource-constrained settings, addressing nonconvex problems through iterative refinement while maintaining the formal guarantees essential for safety-critical applications. While emerging machine learning (ML) methods offer potential enhancements to trajectory generation, they often lack these rigorous guarantees. To address this, we propose a hybrid trajectory optimization framework for robotic servicers, using autoregressive trajectory-generator networks to produce high-quality initial guesses and warm-start an SCP module, enabling the system to produce optimal trajectories quickly and reliably. A key advantage of this approach is the elimination of inverse-kinematics optimization for redundant manipulators during both guess generation and subsequent refinement. By conditioning on exogenous inputs shared with the SCP solver, the networks are inherently task- and obstacle-aware, yielding a tightly integrated architecture that minimizes on-board computational requirements. Results demonstrate that this network-based warm-starting strategy substantially accelerates trajectory generation, reducing both SCP computational time and iterations, while preserving the theoretical guarantees of convex optimization.
D’Ambrosio et al. (Fri,) studied this question.