Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation

• URL: http://arxiv.org/abs/2202.01069v2
• Date: Wed, 4 Oct 2023 16:14:51 GMT
• Title: Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models, Benchmark and Efficient Evaluation
• Authors: Marco Rosano, Antonino Furnari, Luigi Gulino, Corrado Santoro, Giovanni Maria Farinella
• Abstract summary: In recent learning-based navigation approaches, the scene understanding and navigation abilities of the agent are achieved simultaneously. Unfortunately, even if simulators represent an efficient tool to train navigation policies, the resulting models often fail when transferred into the real world. One possible solution is to provide the navigation model with mid-level visual representations containing important domain-invariant properties of the scene.
• Score: 13.207579081178716
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Navigating complex indoor environments requires a deep understanding of the space the robotic agent is acting into to correctly inform the navigation process of the agent towards the goal location. In recent learning-based navigation approaches, the scene understanding and navigation abilities of the agent are achieved simultaneously by collecting the required experience in simulation. Unfortunately, even if simulators represent an efficient tool to train navigation policies, the resulting models often fail when transferred into the real world. One possible solution is to provide the navigation model with mid-level visual representations containing important domain-invariant properties of the scene. But, what are the best representations that facilitate the transfer of a model to the real-world? How can they be combined? In this work we address these issues by proposing a benchmark of Deep Learning architectures to combine a range of mid-level visual representations, to perform a PointGoal navigation task following a Reinforcement Learning setup. All the proposed navigation models have been trained with the Habitat simulator on a synthetic office environment and have been tested on the same real-world environment using a real robotic platform. To efficiently assess their performance in a real context, a validation tool has been proposed to generate realistic navigation episodes inside the simulator. Our experiments showed that navigation models can benefit from the multi-modal input and that our validation tool can provide good estimation of the expected navigation performance in the real world, while saving time and resources. The acquired synthetic and real 3D models of the environment, together with the code of our validation tool built on top of Habitat, are publicly available at the following link: https://iplab.dmi.unict.it/EmbodiedVN/

Related papers

• Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks [93.38375271826202]
  We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks. We first build a simulator by integrating Gaussian splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks. In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, programming of expert demonstration training data, and the task understanding capabilities of Liquid networks.
  arXiv  Detail & Related papers  (2024-06-21T13:48:37Z)
• Interactive Semantic Map Representation for Skill-based Visual Object Navigation [43.71312386938849]
  This paper introduces a new representation of a scene semantic map formed during the embodied agent interaction with the indoor environment. We have implemented this representation into a full-fledged navigation approach called SkillTron. The proposed approach makes it possible to form both intermediate goals for robot exploration and the final goal for object navigation.
  arXiv  Detail & Related papers  (2023-11-07T16:30:12Z)
• Learning Navigational Visual Representations with Semantic Map Supervision [85.91625020847358]
  We propose a navigational-specific visual representation learning method by contrasting the agent's egocentric views and semantic maps. Ego$2$-Map learning transfers the compact and rich information from a map, such as objects, structure and transition, to the agent's egocentric representations for navigation.
  arXiv  Detail & Related papers  (2023-07-23T14:01:05Z)
• Navigating to Objects in the Real World [76.1517654037993]
  We present a large-scale empirical study of semantic visual navigation methods comparing methods from classical, modular, and end-to-end learning approaches. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality.
  arXiv  Detail & Related papers  (2022-12-02T01:10:47Z)
• Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation [98.51313127382937]
  We focus on the use of labels in the synthetic domain alone. Our approach introduces both a way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data.
  arXiv  Detail & Related papers  (2021-11-15T18:37:43Z)
• Out of the Box: Embodied Navigation in the Real World [45.97756658635314]
  We show how to transfer knowledge acquired in simulation into the real world. We deploy our models on a LoCoBot equipped with a single Intel RealSense camera. Our experiments indicate that it is possible to achieve satisfying results when deploying the obtained model in the real world.
  arXiv  Detail & Related papers  (2021-05-12T18:00:14Z)
• Unsupervised Domain Adaptation for Visual Navigation [115.85181329193092]
  We propose an unsupervised domain adaptation method for visual navigation. Our method translates the images in the target domain to the source domain such that the translation is consistent with the representations learned by the navigation policy.
  arXiv  Detail & Related papers  (2020-10-27T18:22:43Z)
• On Embodied Visual Navigation in Real Environments Through Habitat [20.630139085937586]
  Visual navigation models based on deep learning can learn effective policies when trained on large amounts of visual observations. To deal with this limitation, several simulation platforms have been proposed in order to train visual navigation policies on virtual environments efficiently. We show that our tool can effectively help to train and evaluate navigation policies on real-world observations without running navigation pisodes in the real world.
  arXiv  Detail & Related papers  (2020-10-26T09:19:07Z)
• Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments [20.017277077448924]
  NavACL is a method of automatic curriculum learning tailored to the navigation task. Deep reinforcement learning agents trained using NavACL significantly outperform state-of-the-art agents trained with uniform sampling. Our agents can navigate through unknown cluttered indoor environments to semantically-specified targets using only RGB images.
  arXiv  Detail & Related papers  (2020-09-11T13:28:26Z)
• Object Goal Navigation using Goal-Oriented Semantic Exploration [98.14078233526476]
  This work studies the problem of object goal navigation which involves navigating to an instance of the given object category in unseen environments. We propose a modular system called, Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently.
  arXiv  Detail & Related papers  (2020-07-01T17:52:32Z)

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.