Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments

• URL: http://arxiv.org/abs/2309.09864v1
• Date: Mon, 18 Sep 2023 15:24:55 GMT
• Title: Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments
• Authors: Daria de Tinguy, Toon Van de Maele, Tim Verbelen, Bart Dhoedt
• Abstract summary: This paper introduces a hierarchical active inference model addressing the challenge of inferring structure in the world from pixel-based observations. We propose a three-layer hierarchical model consisting of a cognitive map, an allocentric, and an egocentric world model, combining curiosity-driven exploration with goal-oriented behaviour.
• Score: 8.301959009586861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cognitive maps play a crucial role in facilitating flexible behaviour by representing spatial and conceptual relationships within an environment. The ability to learn and infer the underlying structure of the environment is crucial for effective exploration and navigation. This paper introduces a hierarchical active inference model addressing the challenge of inferring structure in the world from pixel-based observations. We propose a three-layer hierarchical model consisting of a cognitive map, an allocentric, and an egocentric world model, combining curiosity-driven exploration with goal-oriented behaviour at the different levels of reasoning from context to place to motion. This allows for efficient exploration and goal-directed search in room-structured mini-grid environments.

Related papers

• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Exploring and Learning Structure: Active Inference Approach in Navigational Agents [8.301959009586861]
  Animals exhibit remarkable navigation abilities by efficiently using memory, imagination, and strategic decision-making. We introduce a novel computational model for navigation and mapping rooted in biologically inspired principles.
  arXiv  Detail & Related papers  (2024-08-12T08:17:14Z)
• Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
  This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
  arXiv  Detail & Related papers  (2024-07-22T12:32:09Z)
• Dynamic planning in hierarchical active inference [0.0]
  We refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. This study distances from traditional views centered on neural networks and reinforcement learning, and points toward a yet unexplored direction in active inference.
  arXiv  Detail & Related papers  (2024-02-18T17:32:53Z)
• 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)
• Inferring Hierarchical Structure in Multi-Room Maze Environments [4.6956495676681484]
  This paper introduces a hierarchical active inference model addressing the challenge of inferring structure in the world from pixel-based observations. We propose a three-layer hierarchical model consisting of a cognitive map, an allocentric, and an egocentric world model, combining curiosity-driven exploration with goal-oriented behaviour.
  arXiv  Detail & Related papers  (2023-06-23T15:15:57Z)
• Unsupervised Discriminative Embedding for Sub-Action Learning in Complex Activities [54.615003524001686]
  This paper proposes a novel approach for unsupervised sub-action learning in complex activities. The proposed method maps both visual and temporal representations to a latent space where the sub-actions are learnt discriminatively. We show that the proposed combination of visual-temporal embedding and discriminative latent concepts allow to learn robust action representations in an unsupervised setting.
  arXiv  Detail & Related papers  (2021-04-30T20:07:27Z)
• Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps [0.0]
  Humans learn by exploration and imitation, build causal models of the world, and use both to flexibly solve new tasks. We suggest a simple but effective unsupervised model which develops such characteristics. We demonstrate its performance on a set of several related, but different one-shot imitation tasks, which the agent flexibly solves in an active inference style.
  arXiv  Detail & Related papers  (2020-07-03T12:29:11Z)
• 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)
• Neural Topological SLAM for Visual Navigation [112.73876869904]
  We design topological representations for space that leverage semantics and afford approximate geometric reasoning. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation.
  arXiv  Detail & Related papers  (2020-05-25T17:56:29Z)
• Progressive growing of self-organized hierarchical representations for exploration [22.950651316748207]
  A central challenge is how to learn representations in order to progressively build a map of the discovered structures. We aim to build lasting representations and avoid catastrophic forgetting throughout the exploration process. Thirdly, we target representations that can structure the agent discoveries in a coarse-to-fine manner.
  arXiv  Detail & Related papers  (2020-05-13T15:24:42Z)

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.