Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers

• URL: http://arxiv.org/abs/2410.11723v1
• Date: Tue, 15 Oct 2024 15:55:42 GMT
• Title: Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers
• Authors: Davide Celestini, Amirhossein Afsharrad, Daniele Gammelli, Tommaso Guffanti, Gioele Zardini, Sanjay Lall, Elisa Capello, Simone D'Amico, Marco Pavone,
• Abstract summary: We present a novel trajectory generation framework that generalizes across diverse problem configurations. We leverage high-capacity transformer neural networks capable of learning from data sources. The framework is validated through simulations and experiments on a free-flyer platform.
• Score: 14.176630393074149
• License:
• Abstract: Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the benefits of optimization- and data-driven methods. Current approaches for learning-based trajectory generation often focus on fixed, single-scenario environments, where key scene characteristics, such as obstacle positions or final-time requirements, remain constant across problem instances. However, practical trajectory generation requires the scenario to be frequently reconfigured, making the single-scenario approach a potentially impractical solution. To address this challenge, we present a novel trajectory generation framework that generalizes across diverse problem configurations, by leveraging high-capacity transformer neural networks capable of learning from multimodal data sources. Specifically, our approach integrates transformer-based neural network models into the trajectory optimization process, encoding both scene-level information (e.g., obstacle locations, initial and goal states) and trajectory-level constraints (e.g., time bounds, fuel consumption targets) via multimodal representations. The transformer network then generates near-optimal initial guesses for non-convex optimization problems, significantly enhancing convergence speed and performance. The framework is validated through extensive simulations and real-world experiments on a free-flyer platform, achieving up to 30% cost improvement and 80% reduction in infeasible cases with respect to traditional approaches, and demonstrating robust generalization across diverse scenario variations.

Related papers

• Teleportation With Null Space Gradient Projection for Optimization Acceleration [31.641252776379957]
  We introduce an algorithm that projects the gradient of the teleportation objective function onto the input null space. Our approach is readily generalizable from CNNs to transformers, and potentially other advanced architectures.
  arXiv  Detail & Related papers  (2025-02-17T02:27:16Z)
• Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models [57.45019514036948]
  Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics. This work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces.
  arXiv  Detail & Related papers  (2024-12-23T21:27:19Z)
• A Coalition Game for On-demand Multi-modal 3D Automated Delivery System [4.378407481656902]
  We introduce a multi-modal autonomous delivery optimization framework as a coalition game for a fleet of UAVs and ADRs operating in two overlaying networks. The framework addresses last-mile delivery in urban environments, including high-density areas, road-based routing, and real-world operational challenges.
  arXiv  Detail & Related papers  (2024-12-23T03:50:29Z)
• Preventing Local Pitfalls in Vector Quantization via Optimal Transport [77.15924044466976]
  We introduce OptVQ, a novel vector quantization method that employs the Sinkhorn algorithm to optimize the optimal transport problem. Our experiments on image reconstruction tasks demonstrate that OptVQ achieves 100% codebook utilization and surpasses current state-of-the-art VQNs in reconstruction quality.
  arXiv  Detail & Related papers  (2024-12-19T18:58:14Z)
• Towards Robust Spacecraft Trajectory Optimization via Transformers [17.073280827888226]
  We develop an autonomous generative model to solve non- optimal control problems in real-time. We extend the capabilities of ART to address robust chance-constrained optimal control problems. 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)
• Adaptive Class Emergence Training: Enhancing Neural Network Stability and Generalization through Progressive Target Evolution [0.0]
  We propose a novel training methodology for neural networks in classification problems. We evolve the target outputs from a null vector to one-hot encoded vectors throughout the training process. This gradual transition allows the network to adapt more smoothly to the increasing complexity of the classification task.
  arXiv  Detail & Related papers  (2024-09-04T03:25:48Z)
• DiffSG: A Generative Solver for Network Optimization with Diffusion Model [75.27274046562806]
  Diffusion generative models can consider a broader range of solutions and exhibit stronger generalization by learning parameters. We propose a new framework, which leverages intrinsic distribution learning of diffusion generative models to learn high-quality solutions.
  arXiv  Detail & Related papers  (2024-08-13T07:56:21Z)
• Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout [0.0]
  Connected and autonomous vehicles have the potential to minimize energy consumption. Existing deterministic and methods created to solve the eco-driving problem generally suffer from high computational and memory requirements. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network.
  arXiv  Detail & Related papers  (2023-10-16T23:13:51Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• Dynamic Federated Learning [57.14673504239551]
  Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm.
  arXiv  Detail & Related papers  (2020-02-20T15:00:54Z)

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.