Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles

• URL: http://arxiv.org/abs/2206.15211v2
• Date: Fri, 1 Jul 2022 01:27:15 GMT
• Title: Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles
• Authors: Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling, Ricardo Bedin Grando, Rodrigo da Silva Guerra, Paulo Lilles Jorge Drews Jr
• Abstract summary: Reinforcement Learning (RL) has presented an impressive performance in video games through raw pixel imaging and continuous control tasks. It is generally accepted that physical state-based RL policies such as laser sensor measurements give a more sample-efficient result than learning by pixels. This work presents a new approach that extracts information from a depth map estimation to teach an RL agent to perform the mapless navigation of Unmanned Aerial Vehicle (UAV)
• Score: 1.2468700211588883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning (RL) has presented an impressive performance in video games through raw pixel imaging and continuous control tasks. However, RL performs poorly with high-dimensional observations such as raw pixel images. It is generally accepted that physical state-based RL policies such as laser sensor measurements give a more sample-efficient result than learning by pixels. This work presents a new approach that extracts information from a depth map estimation to teach an RL agent to perform the mapless navigation of Unmanned Aerial Vehicle (UAV). We propose the Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning(Depth-CUPRL) that estimates the depth of images with a prioritized replay memory. We used a combination of RL and Contrastive Learning to lead with the problem of RL based on images. From the analysis of the results with Unmanned Aerial Vehicles (UAVs), it is possible to conclude that our Depth-CUPRL approach is effective for the decision-making and outperforms state-of-the-art pixel-based approaches in the mapless navigation capability.

Related papers

• An Examination of Offline-Trained Encoders in Vision-Based Deep Reinforcement Learning for Autonomous Driving [0.0]
  Research investigates the challenges Deep Reinforcement Learning (DRL) faces in Partially Observable Markov Decision Processes (POMDP) Our research adopts an offline-trained encoder to leverage large video datasets through self-supervised learning to learn generalizable representations. We show that the features learned by watching BDD100K driving videos can be directly transferred to achieve lane following and collision avoidance in CARLA simulator.
  arXiv  Detail & Related papers  (2024-09-02T14:16:23Z)
• Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN [60.257791714663725]
  We propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. The proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.
  arXiv  Detail & Related papers  (2022-12-23T03:00:28Z)
• An Empirical Study of Remote Sensing Pretraining [117.90699699469639]
  We conduct an empirical study of remote sensing pretraining (RSP) on aerial images. RSP can help deliver distinctive performances in scene recognition tasks. RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, but it may still suffer from task discrepancies.
  arXiv  Detail & Related papers  (2022-04-06T13:38:11Z)
• Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data [1.7894377200944511]
  We present the first-ever demonstration of deep learning-based single image height prediction for the other important sensor modality in remote sensing: synthetic aperture radar (SAR) data. Besides the adaptation of a convolutional neural network (CNN) architecture for SAR intensity images, we present a workflow for the generation of training data. Since we put a particular emphasis on transferability, we are able to confirm that deep learning-based single-image height estimation is not only possible, but also transfers quite well to unseen data.
  arXiv  Detail & Related papers  (2021-11-03T08:20:03Z)
• Homography augumented momentum constrastive learning for SAR image retrieval [3.9743795764085545]
  We propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning. We also propose a training method for the DNNs induced by contrastive learning that does not require any labeling procedure.
  arXiv  Detail & Related papers  (2021-09-21T17:27:07Z)
• Deterministic Guided LiDAR Depth Map Completion [0.0]
  This paper presents a non-deep learning-based approach to densify a sparse LiDAR-based depth map using a guidance RGB image. The evaluation of this work is executed using the KITTI depth completion benchmark, which validates the proposed work.
  arXiv  Detail & Related papers  (2021-06-14T09:19:47Z)
• Memory-Augmented Reinforcement Learning for Image-Goal Navigation [67.3963444878746]
  We present a novel method that leverages a cross-episode memory to learn to navigate. In order to avoid overfitting, we propose to use data augmentation on the RGB input during training. We obtain this competitive performance from RGB input only, without access to additional sensors such as position or depth.
  arXiv  Detail & Related papers  (2021-01-13T16:30:20Z)
• Dual Pixel Exploration: Simultaneous Depth Estimation and Image Restoration [77.1056200937214]
  We study the formation of the DP pair which links the blur and the depth information. We propose an end-to-end DDDNet (DP-based Depth and De Network) to jointly estimate the depth and restore the image.
  arXiv  Detail & Related papers  (2020-12-01T06:53:57Z)
• ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning [16.18256739680704]
  Current reinforcement learning algorithms operate on the whole image without performing object-level reasoning. In this paper, we improve upon previous visual self-supervised RL by incorporating object-level reasoning and occlusion reasoning. Our proposed algorithm, ROLL, learns dramatically faster and better final performance compared with previous methods in several simulated visual control tasks.
  arXiv  Detail & Related papers  (2020-11-13T06:21:56Z)
• Single Image Depth Estimation Trained via Depth from Defocus Cues [105.67073923825842]
  Estimating depth from a single RGB image is a fundamental task in computer vision. In this work, we rely, instead of different views, on depth from focus cues. We present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches.
  arXiv  Detail & Related papers  (2020-01-14T20:22:54Z)
• Learn to Interpret Atari Agents [106.21468537372995]
  Region-sensitive Rainbow (RS-Rainbow) is an end-to-end trainable network based on the original Rainbow, a powerful deep Q-network agent. Our proposed agent, named region-sensitive Rainbow (RS-Rainbow), is an end-to-end trainable network based on the original Rainbow, a powerful deep Q-network agent.
  arXiv  Detail & Related papers  (2018-12-29T03:35:32Z)

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.