Learning Embeddings that Capture Spatial Semantics for Indoor Navigation

• URL: http://arxiv.org/abs/2108.00159v1
• Date: Sat, 31 Jul 2021 06:12:40 GMT
• Title: Learning Embeddings that Capture Spatial Semantics for Indoor Navigation
• Authors: Vidhi Jain, Prakhar Agarwal, Shishir Patil, Katia Sycara
• Abstract summary: We study how object embeddings that capture spatial semantic priors can guide search and navigation tasks in a structured environment. We propose a method to incorporate such spatial semantic awareness in robots by leveraging pre-trained language models and multi-relational knowledge bases as object embeddings.
• Score: 2.2940141855172027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Incorporating domain-specific priors in search and navigation tasks has shown promising results in improving generalization and sample complexity over end-to-end trained policies. In this work, we study how object embeddings that capture spatial semantic priors can guide search and navigation tasks in a structured environment. We know that humans can search for an object like a book, or a plate in an unseen house, based on the spatial semantics of bigger objects detected. For example, a book is likely to be on a bookshelf or a table, whereas a plate is likely to be in a cupboard or dishwasher. We propose a method to incorporate such spatial semantic awareness in robots by leveraging pre-trained language models and multi-relational knowledge bases as object embeddings. We demonstrate using these object embeddings to search a query object in an unseen indoor environment. We measure the performance of these embeddings in an indoor simulator (AI2Thor). We further evaluate different pre-trained embedding onSuccess Rate(SR) and success weighted by Path Length(SPL).

Related papers

• Object Goal Navigation with Recursive Implicit Maps [92.6347010295396]
  We propose an implicit spatial map for object goal navigation. Our method significantly outperforms the state of the art on the challenging MP3D dataset. We deploy our model on a real robot and achieve encouraging object goal navigation results in real scenes.
  arXiv  Detail & Related papers  (2023-08-10T14:21:33Z)
• 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)
• How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers [94.46825166907831]
  We present a training-free solution to tackle the object goal navigation problem in Embodied AI. Our method builds a structured scene representation based on the classic visual simultaneous localization and mapping (V-SLAM) framework. Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
  arXiv  Detail & Related papers  (2023-05-26T13:38:33Z)
• PEANUT: Predicting and Navigating to Unseen Targets [18.87376347895365]
  Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an understanding of the spatial and semantic regularities in environment layouts. We present a method for learning these regularities by predicting the locations of unobserved objects from incomplete semantic maps. Our prediction model is lightweight and can be trained in a supervised manner using a relatively small amount of passively collected data.
  arXiv  Detail & Related papers  (2022-12-05T18:58:58Z)
• A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations [20.15854546504947]
  We propose a modular framework for object-nav that is able to efficiently search indoor environments for not just static objects but also movable objects. Our contextual-bandit agent efficiently explores the environment by showing optimism in the face of uncertainty. We evaluate our algorithms in two simulated environments and a real-world setting, to demonstrate high sample efficiency and reliability.
  arXiv  Detail & Related papers  (2022-11-29T15:48:54Z)
• Structured Exploration Through Instruction Enhancement for Object Navigation [0.0]
  We propose a hierarchical learning-based method for object navigation. The top-level is capable of high-level planning, and building a memory on a floorplan-level. We demonstrate the effectiveness of our method on a dynamic domestic environment.
  arXiv  Detail & Related papers  (2022-11-15T19:39:22Z)
• Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation [87.52136927091712]
  We address a practical yet challenging problem of training robot agents to navigate in an environment following a path described by some language instructions. To achieve accurate and efficient navigation, it is critical to build a map that accurately represents both spatial location and the semantic information of the environment objects. We propose a multi-granularity map, which contains both object fine-grained details (e.g., color, texture) and semantic classes, to represent objects more comprehensively.
  arXiv  Detail & Related papers  (2022-10-14T04:23:27Z)
• Landmark Policy Optimization for Object Navigation Task [77.34726150561087]
  This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments. Recent works have shown significant achievements both in the end-to-end Reinforcement Learning approach and modular systems, but need a big step forward to be robust and optimal. We propose a hierarchical method that incorporates standard task formulation and additional area knowledge as landmarks, with a way to extract these landmarks.
  arXiv  Detail & Related papers  (2021-09-17T12:28:46Z)
• Learning to Map for Active Semantic Goal Navigation [40.193928212509356]
  We propose a novel framework that actively learns to generate semantic maps outside the field of view of the agent. We show how different objectives can be defined by balancing exploration with exploitation. Our method is validated in the visually realistic environments offered by the Matterport3D dataset.
  arXiv  Detail & Related papers  (2021-06-29T18:01:30Z)
• 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)
• Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues [12.984393386954219]
  This paper addresses the problem of building augmented metric representations of scenes with semantic information from RGB-D images. We propose a complete framework to create an enhanced map representation of the environment with object-level information.
  arXiv  Detail & Related papers  (2020-03-13T15:05:23Z)

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.