Improving Path Planning Performance through Multimodal Generative Models with Local Critics

• URL: http://arxiv.org/abs/2306.09470v1
• Date: Thu, 15 Jun 2023 19:51:35 GMT
• Title: Improving Path Planning Performance through Multimodal Generative Models with Local Critics
• Authors: Jorge Ocampo Jimenez and Wael Suleiman
• Abstract summary: This paper presents a novel method for accelerating path planning tasks in unknown scenes with obstacles. We use Wasserstein Generative Adversarial Networks (WGANs) with Gradient Penalty (GP) to approximate the distribution of the free conditioned configuration space. Our experiments show promising results for accelerating path planning tasks in unknown scenes while generating quasi-optimal paths with our WGAN-GP.
• Score: 1.3706331473063877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a novel method for accelerating path planning tasks in unknown scenes with obstacles by utilizing Wasserstein Generative Adversarial Networks (WGANs) with Gradient Penalty (GP) to approximate the distribution of the free conditioned configuration space. Our proposed approach involves conditioning the WGAN-GP with a Variational Auto-Encoder in a continuous latent space to handle multimodal datasets. However, training a Variational Auto-Encoder with WGAN-GP can be challenging for image-to-configuration-space problems, as the Kullback-Leibler loss function often converges to a random distribution. To overcome this issue, we simplify the configuration space as a set of Gaussian distributions and divide the dataset into several local models. This enables us to not only learn the model but also speed up its convergence. We evaluate the reconstructed configuration space using the homology rank of manifolds for datasets with the geometry score. Furthermore, we propose a novel transformation of the robot's configuration space that enables us to measure how well collision-free regions are reconstructed, which could be used with other rank of homology metrics. Our experiments show promising results for accelerating path planning tasks in unknown scenes while generating quasi-optimal paths with our WGAN-GP. The source code is openly available.

Related papers

• Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional Systems [3.6773638205393198]
  Efficient inference in high-dimensional models is a central challenge in machine learning. We introduce the Ensemble Kalman Filter (EnKF) and Gaussian Belief Propagation (GaBP) GEnBP updates ensembles of prior samples into posterior samples by passing low-rank local messages over the edges of a graphical model.
  arXiv  Detail & Related papers  (2024-02-13T03:31:36Z)
• NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain [5.337162499594818]
  We propose the NNPP model for computing the region, enabling foundation algorithms like Astar to find the optimal path solely within this reduced search space. The NNPP model learns information about start and goal locations, as well as map representations, from numerous pre-annotated optimal path demonstrations. It is able to textcolorrevisionaccelerate path planning on novel maps.
  arXiv  Detail & Related papers  (2023-08-09T08:31:05Z)
• GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization [52.55628139825667]
  Federated Learning (FL) has emerged as a promising distributed machine learning framework to preserve clients' privacy. Recent studies find that an attacker can invert the shared gradients and recover sensitive data against an FL system by leveraging pre-trained generative adversarial networks (GAN) as prior knowledge. We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD), which disassembles the GAN model and searches the feature domains of the intermediate layers.
  arXiv  Detail & Related papers  (2023-08-09T04:34:21Z)
• Complexity Matters: Rethinking the Latent Space for Generative Modeling [65.64763873078114]
  In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity.
  arXiv  Detail & Related papers  (2023-07-17T07:12:29Z)
• VTAE: Variational Transformer Autoencoder with Manifolds Learning [144.0546653941249]
  Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables. The nonlinearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. We show that geodesics and accurate computation can substantially improve the performance of deep generative models.
  arXiv  Detail & Related papers  (2023-04-03T13:13:19Z)
• DDPEN: Trajectory Optimisation With Sub Goal Generation Model [70.36888514074022]
  In this paper, we produce a novel Differential Dynamic Programming with Escape Network (DDPEN) We propose to utilize a deep model that takes as an input map of the environment in the form of a costmap together with the desired position. The model produces possible future directions that will lead to the goal, avoiding local minima which is possible to run in real time conditions.
  arXiv  Detail & Related papers  (2023-01-18T11:02:06Z)
• Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment [130.84010267004803]
  Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption. We propose a relaxed spatial structural alignment method to calibrate the target generative models during the adaption.
  arXiv  Detail & Related papers  (2022-03-06T14:26:25Z)
• Non-Gaussian Gaussian Processes for Few-Shot Regression [71.33730039795921]
  We propose an invertible ODE-based mapping that operates on each component of the random variable vectors and shares the parameters across all of them. NGGPs outperform the competing state-of-the-art approaches on a diversified set of benchmarks and applications.
  arXiv  Detail & Related papers  (2021-10-26T10:45:25Z)
• Encoding spatiotemporal priors with VAEs for small-area estimation [2.4783465852664324]
  We propose a deep generative modelling approach to tackle a noveltemporal setting. We approximate a class of prior samplings through prior fitting of a variational autoencoder (VAE) VAE allows inference to become incredibly efficient due to independently distributed latent latent Gaussian space representation. We demonstrate the utility of our VAE two stage approach on Bayesian, small-area estimation tasks.
  arXiv  Detail & Related papers  (2021-10-20T08:14:15Z)
• Generative Adversarial Network based Heuristics for Sampling-based Path Planning [34.368519009432426]
  We present a novel image-based path planning algorithm to overcome limitations of sampling-based path planning. Specifically, a generative adversarial network (GAN) is designed to take the environment map as the input without other preprocessing works. We conduct a number of simulation experiments to validate the effectiveness of the proposed method, and the results demonstrate that our method performs much better in terms of the quality of initial solution and the convergence speed to the optimal solution.
  arXiv  Detail & Related papers  (2020-12-07T07:29:57Z)

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.