[Paper Review] Improving Path Planning Performance through Multimodal Generative Models with Local Critics

Jorge Ocampo Jimenez, Wael Suleiman|arXiv (Cornell University)|Jun 15, 2023

Human Pose and Action RecognitionComputer Science3 citations

TL;DR

This paper proposes a multimodal generative model using Wasserstein GANs with gradient penalty (WGAN-GP) conditioned by a Variational Autoencoder (VAE) to accelerate path planning in unknown, obstacle-filled environments. By dividing the configuration space into local Gaussian models and employing local critics, the method improves training stability, accelerates convergence, and enables quasi-optimal path generation in unseen scenarios, outperforming standard WGAN-GP-VAE approaches in reconstructing collision-free configuration spaces.

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.

Motivation & Objective

• To address the challenge of slow and inefficient sampling-based path planning in complex, unknown configuration spaces with obstacles.
• To improve the training stability and convergence of GANs conditioned on image inputs for modeling multimodal collision-free configuration spaces.
• To enable generalization to unseen scenarios by learning a continuous latent space that interpolates between known obstacle configurations.
• To develop a robust evaluation metric based on homology rank to quantify the quality of configuration space reconstruction.
• To enhance path planning efficiency by generating quasi-optimal, collision-free paths using learned generative models.

Proposed method

• Condition the WGAN-GP generator on a VAE-learned latent vector derived from RGB images of obstacle configurations to enable image-to-configuration-space translation.
• Decompose the configuration space into local regions modeled as Gaussian distributions to simplify training and prevent mode collapse.
• Use local critics in the WGAN-GP framework to improve training stability and distribution approximation in each local region of the configuration space.
• Apply a Wasserstein loss with gradient penalty to stabilize training and avoid mode collapse, especially in high-dimensional, multimodal settings.
• Transform the complement of the reconstructed configuration space to compute homology rank, enabling topological evaluation of collision-free region reconstruction.
• Train the VAE and WGAN-GP models independently to reduce complexity and improve convergence, leveraging the continuous latent space for zero-shot generalization to unseen obstacle layouts.

Experimental results

Research questions

• RQ1 Can WGAN-GP with VAE conditioning effectively model multimodal, image-conditioned configuration spaces for robot path planning?
• RQ2 How does dividing the configuration space into local Gaussian models improve training stability and convergence of the generative model?
• RQ3 To what extent do local critics enhance the quality of configuration space reconstruction and path planning performance?
• RQ4 Can homology rank metrics accurately assess the topological fidelity of generated collision-free configuration spaces?
• RQ5 How well does the model generalize to unseen obstacle configurations not present in the training data?

Key findings

• The proposed MultiWGAN-GP with local critics and VAE conditioning achieved faster and more stable training compared to standard WGAN-GP-VAE models.
• The model successfully reconstructed the topology of collision-free configuration spaces, as validated by homology rank metrics, indicating accurate capture of holes and clusters in the free space.
• The method generated quasi-optimal paths in unseen scenarios with a higher success rate than baseline RRT and RRT* algorithms.
• Local critics significantly improved the distribution approximation, reducing mode collapse and enhancing the diversity and validity of generated configuration states.
• The decomposition into local models enabled faster convergence by reducing the complexity of learning each region’s distribution as a Gaussian translation.
• The model demonstrated effective zero-shot generalization, generating valid paths for obstacle layouts not seen during training, thanks to the continuous latent space learned by the VAE.