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

• URL: http://arxiv.org/abs/2606.17317v1
• Date: Mon, 15 Jun 2026 21:54:43 GMT
• Title: Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators
• Abstract summary: Real-time trajectory guidance for space manipulators is challenging due to the nonlinear coupling between spacecraft bus motion, manipulator dynamics, cone, robustness, and safety constraints.<n>This paper introduces a sequential learning-based warmally-cost-model SCP-chunk warm-starts.<n>Results show that SCP-chunk warm-starts can improve both the efficiency and trajectory guidance of SCP-based terminal programming for space manipulation.
• Score: 9.91824382053006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Event-Based Motion Estimation via Oriented Distance Fields [55.23416502666468]
  Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion.<n>We propose Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors.<n>On public and self-collected datasets, ODF motion estimation reaches sub-pixel accuracy at the lowest latency among compared methods.
  arXiv  Detail & Related papers  (2026-08-25T08:27:42Z)
• Constant Time-Delay Leader Following with Neural Networks and Invariant Extended Kalman Filters for Arbitrary Trajectories [0.9176056742068813]
  This paper proposes a constant time-delay trajectory tracking method for vehicle convoys operating without inter-vehicle communication.<n>The method integrates a probabilistic sequence-to-sequence (Seq2Seq) neural network with an invariant extended Kalman filter (IEKF) to warm-start the prediction process.<n>The effectiveness of the method is validated through comparisons with a pure IEKF baseline, learning-based methods, and the ground-truth trajectory in kinematic simulations.
  arXiv  Detail & Related papers  (2026-06-17T16:04:58Z)
• Planning-aligned Token Compression for Long-Context Autonomous Driving [95.59023657139208]
  We propose a planning-aligned working memory framework built on conditional VQ-VA.<n>Compression is conditioned on both historical trajectory and a learned planning intent.<n>We evaluate on high-signal dynamic scenarios where historical context is most critical for behavior correctness.
  arXiv  Detail & Related papers  (2026-06-05T17:16:21Z)
• GPU-Accelerated Continuous-Time Successive Convexification for Contact-Implicit Legged Locomotion [0.4588028371034407]
  Contact-implicit trajectory optimization (CITO) enables the automatic discovery of contact sequences.<n>We introduce integral cross-compITOlementarity constraints, which eliminate the risk of missing contact events between discretization nodes.<n>We show that the resulting framework, contact-implicit successive convexification (ci-SCvx), models full multibody dynamics in maximal coordinates.
  arXiv  Detail & Related papers  (2026-04-11T02:40:29Z)
• RegFormer++: An Efficient Large-Scale 3D LiDAR Point Registration Network with Projection-Aware 2D Transformer [71.59462491691819]
  We propose a novel end-to-end differential transformer network, termed RegFormer++, for large-scale point cloud alignment.<n>Because we fill original 3D coordinates into 2D projected positions, our designed transformer can benefit from both high efficiency in 2D processing and accuracy from 3D geometric information.<n>Experiments on KITTI, NuScenes, and Argoverse datasets demonstrate that our model achieves state-of-the-art performance in terms of both accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-03-15T09:03:10Z)
• DELTAv2: Accelerating Dense 3D Tracking [79.63990337419514]
  We propose a novel algorithm for accelerating dense long-term 3D point tracking in videos.<n>We introduce a coarse-to-fine strategy that begins tracking with a small subset of points and progressively expands the set of tracked trajectories.<n>The newly added trajectories are using a learnable module, which is trained end-to-end alongside the tracking network.
  arXiv  Detail & Related papers  (2025-08-02T03:15:47Z)
• ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions [91.55655961014027]
  3D semantic occupancy and flow prediction are fundamental to understanding scene scene.<n>This paper proposes a vision-based framework with three targeted improvements.<n>Our purely convolutional architecture establishes new SOTA performance on multiple benchmarks for both semantic occupancy and joint semantic-flow prediction.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• Towards Robust Spacecraft Trajectory Optimization via Transformers [17.073280827888226]
  We develop an autonomous generative model to solve non- optimal control problems in real-time.<n>We extend the capabilities of ART to address robust chance-constrained optimal control problems.<n>This work marks an initial step toward the reliable deployment of AI-driven solutions in safety-critical autonomous systems such as spacecraft.
  arXiv  Detail & Related papers  (2024-10-08T00:58:42Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• ICP-Flow: LiDAR Scene Flow Estimation with ICP [2.9290232815049926]
  Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. We propose ICP-Flow, a learning-free flow estimator, to associate objects over scans and then estimate the locally rigid transformations. We outperform state-of-the-art baselines, including supervised models, on the dataset and perform competitively on Argoverse-v2 and nuScenes.
  arXiv  Detail & Related papers  (2024-02-27T09:41:59Z)
• Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint Prediction [1.2074552857379275]
  Transformer-based Powered Descent Guidance (T-PDG) is a scalable algorithm for reducing the computational complexity of the direct optimization formulation of the spacecraft powered descent guidance problem. T-PDG uses data from prior runs of trajectory optimization algorithms to train a transformer neural network, which accurately predicts the relationship between problem parameters. A safe and optimal solution is guaranteed by including a feasibility check in T-PDG before returning the final trajectory.
  arXiv  Detail & Related papers  (2023-11-09T04:26:25Z)
• Layer Pruning on Demand with Intermediate CTC [50.509073206630994]
  We present a training and pruning method for ASR based on the connectionist temporal classification (CTC) We show that a Transformer-CTC model can be pruned in various depth on demand, improving real-time factor from 0.005 to 0.002 on GPU.
  arXiv  Detail & Related papers  (2021-06-17T02:40:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.