IN-Sight: Interactive Navigation through Sight

• URL: http://arxiv.org/abs/2408.00343v2
• Date: Mon, 12 Aug 2024 10:19:08 GMT
• Title: IN-Sight: Interactive Navigation through Sight
• Authors: Philipp Schoch, Fan Yang, Yuntao Ma, Stefan Leutenegger, Marco Hutter, Quentin Leboutet,
• Abstract summary: IN-Sight is a novel approach to self-supervised path planning. It calculates traversability scores and incorporates them into a semantic map. To precisely navigate around obstacles, IN-Sight employs a local planner.
• Score: 20.184155117341497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current visual navigation systems often treat the environment as static, lacking the ability to adaptively interact with obstacles. This limitation leads to navigation failure when encountering unavoidable obstructions. In response, we introduce IN-Sight, a novel approach to self-supervised path planning, enabling more effective navigation strategies through interaction with obstacles. Utilizing RGB-D observations, IN-Sight calculates traversability scores and incorporates them into a semantic map, facilitating long-range path planning in complex, maze-like environments. To precisely navigate around obstacles, IN-Sight employs a local planner, trained imperatively on a differentiable costmap using representation learning techniques. The entire framework undergoes end-to-end training within the state-of-the-art photorealistic Intel SPEAR Simulator. We validate the effectiveness of IN-Sight through extensive benchmarking in a variety of simulated scenarios and ablation studies. Moreover, we demonstrate the system's real-world applicability with zero-shot sim-to-real transfer, deploying our planner on the legged robot platform ANYmal, showcasing its practical potential for interactive navigation in real environments.

Related papers

• TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation [5.484041860401147]
  TOP-Nav is a novel legged navigation framework that integrates a comprehensive path planner with Terrain awareness, Obstacle avoidance and close-loop Proprioception. We show that TOP-Nav achieves open-world navigation that the robot can handle terrains or disturbances beyond the distribution of prior knowledge.
  arXiv  Detail & Related papers  (2024-04-23T17:42:45Z)
• 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)
• ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments [56.194988818341976]
  Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
  arXiv  Detail & Related papers  (2023-04-06T13:07:17Z)
• ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation [75.13546386761153]
  We present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC) ESC transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience. Experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines.
  arXiv  Detail & Related papers  (2023-01-30T18:37:32Z)
• Augmented reality navigation system for visual prosthesis [67.09251544230744]
  We propose an augmented reality navigation system for visual prosthesis that incorporates a software of reactive navigation and path planning. It consists on four steps: locating the subject on a map, planning the subject trajectory, showing it to the subject and re-planning without obstacles. Results show how our augmented navigation system help navigation performance by reducing the time and distance to reach the goals, even significantly reducing the number of obstacles collisions.
  arXiv  Detail & Related papers  (2021-09-30T09:41:40Z)
• ViNG: Learning Open-World Navigation with Visual Goals [82.84193221280216]
  We propose a learning-based navigation system for reaching visually indicated goals. We show that our system, which we call ViNG, outperforms previously-proposed methods for goal-conditioned reinforcement learning. We demonstrate ViNG on a number of real-world applications, such as last-mile delivery and warehouse inspection.
  arXiv  Detail & Related papers  (2020-12-17T18:22:32Z)
• 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)
• Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning [32.454250811667904]
  Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments. We present an approach based on reinforcement learning to learn policies capable of dynamic adaptation to the presence of moving pedestrians. We show transfer of the learned policy to unseen 3D reconstructions of two real environments.
  arXiv  Detail & Related papers  (2020-10-16T19:40:08Z)
• 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)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

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.