Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints

• URL: http://arxiv.org/abs/2410.04261v1
• Date: Sat, 5 Oct 2024 18:47:50 GMT
• Title: Compositional Diffusion Models for Powered Descent Trajectory Generation with Flexible Constraints
• Authors: Julia Briden, Yilun Du, Enrico M. Zucchelli, Richard Linares,
• Abstract summary: TrajDiffuser is a compositional diffusion-based flexible and concurrent trajectory generator. It learns the multi-modal distributions of a dataset of simulated optimal trajectories. During inference, the trajectory is generated simultaneously over time, providing stable long-horizon planning.
• Score: 24.530914372991273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces TrajDiffuser, a compositional diffusion-based flexible and concurrent trajectory generator for 6 degrees of freedom powered descent guidance. TrajDiffuser is a statistical model that learns the multi-modal distributions of a dataset of simulated optimal trajectories, each subject to only one or few constraints that may vary for different trajectories. During inference, the trajectory is generated simultaneously over time, providing stable long-horizon planning, and constraints can be composed together, increasing the model's generalizability and decreasing the training data required. The generated trajectory is then used to initialize an optimizer, increasing its robustness and speed.

Related papers

• Constrained Diffusion Models via Dual Training [80.03953599062365]
  Diffusion processes are prone to generating samples that reflect biases in a training dataset. We develop constrained diffusion models by imposing diffusion constraints based on desired distributions. We show that our constrained diffusion models generate new data from a mixture data distribution that achieves the optimal trade-off among objective and constraints.
  arXiv  Detail & Related papers  (2024-08-27T14:25:42Z)
• On the Trajectory Regularity of ODE-based Diffusion Sampling [79.17334230868693]
  Diffusion-based generative models use differential equations to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models.
  arXiv  Detail & Related papers  (2024-05-18T15:59:41Z)
• Flexible Multi-Generator Model with Fused Spatiotemporal Graph for Trajectory Prediction [2.1638817206926855]
  Trajectory prediction plays a vital role in automotive radar systems. Generative adversarial networks with the ability to learn a distribution over future trajectories tend to predict out-of-distribution samples. We propose a trajectory prediction framework, which can capture the social interaction and model disconnected variations of pedestrian trajectories.
  arXiv  Detail & Related papers  (2023-11-06T02:46:05Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• A Geometric Perspective on Diffusion Models [57.27857591493788]
  We inspect the ODE-based sampling of a popular variance-exploding SDE. We establish a theoretical relationship between the optimal ODE-based sampling and the classic mean-shift (mode-seeking) algorithm.
  arXiv  Detail & Related papers  (2023-05-31T15:33:16Z)
• Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting [11.680589359294972]
  We propose TrendGCN to address the challenge of balancing dynamics and robustness. Our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions. Compared with traditional approaches that handle step-wise predictive errors independently, our approach can produce more realistic and robust forecasts.
  arXiv  Detail & Related papers  (2022-08-05T09:36:55Z)
• Manifold Interpolating Optimal-Transport Flows for Trajectory Inference [64.94020639760026]
  We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) MIOFlow learns, continuous population dynamics from static snapshot samples taken at sporadic timepoints. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.
  arXiv  Detail & Related papers  (2022-06-29T22:19:03Z)
• Flow-based Spatio-Temporal Structured Prediction of Motion Dynamics [21.24885597341643]
  Conditional Flows (CNFs) are flexible generative models capable of representing complicated distributions with high dimensionality and interdimensional correlations. We propose MotionFlow as a novel approach that autoregressively normalizes the output on the temporal input features. We apply our method to different tasks, including prediction, motion prediction time series forecasting, and binary segmentation.
  arXiv  Detail & Related papers  (2021-04-09T14:30:35Z)
• Haar Wavelet based Block Autoregressive Flows for Trajectories [129.37479472754083]
  Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. We introduce a novel Haar wavelet based block autoregressive model leveraging split couplings. We illustrate the advantages of our approach for generating diverse and accurate trajectories on two real-world datasets.
  arXiv  Detail & Related papers  (2020-09-21T13:57:10Z)

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.