ForesightNav: Learning Scene Imagination for Efficient Exploration

• URL: http://arxiv.org/abs/2504.16062v2
• Date: Tue, 29 Apr 2025 14:31:24 GMT
• Title: ForesightNav: Learning Scene Imagination for Efficient Exploration
• Authors: Hardik Shah, Jiaxu Xing, Nico Messikommer, Boyang Sun, Marc Pollefeys, Davide Scaramuzza,
• Abstract summary: We propose ForesightNav, a novel exploration strategy inspired by human imagination and reasoning.<n>Our approach equips robotic agents with the capability to predict contextual information, such as occupancy and semantic details, for unexplored regions.<n>We validate our imagination-based approach using the Structured3D dataset, demonstrating accurate occupancy prediction and superior performance in anticipating unseen scene geometry.
• Score: 57.49417653636244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding how humans leverage prior knowledge to navigate unseen environments while making exploratory decisions is essential for developing autonomous robots with similar abilities. In this work, we propose ForesightNav, a novel exploration strategy inspired by human imagination and reasoning. Our approach equips robotic agents with the capability to predict contextual information, such as occupancy and semantic details, for unexplored regions. These predictions enable the robot to efficiently select meaningful long-term navigation goals, significantly enhancing exploration in unseen environments. We validate our imagination-based approach using the Structured3D dataset, demonstrating accurate occupancy prediction and superior performance in anticipating unseen scene geometry. Our experiments show that the imagination module improves exploration efficiency in unseen environments, achieving a 100% completion rate for PointNav and an SPL of 67% for ObjectNav on the Structured3D Validation split. These contributions demonstrate the power of imagination-driven reasoning for autonomous systems to enhance generalizable and efficient exploration.

Related papers

• Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach [23.52028824411467]
  We present a large-scale experimental study involving numepisodes navigation episodes in a real environment with a physical robot.<n>We analyze the type of reasoning emerging from end-to-end training.<n>We show in a post-hoc analysis that the value function learned by the agent relates to long-term planning.
  arXiv  Detail & Related papers  (2025-03-11T11:16:47Z)
• NavigateDiff: Visual Predictors are Zero-Shot Navigation Assistants [24.689242976554482]
  Navigating unfamiliar environments presents significant challenges for household robots. Existing reinforcement learning methods cannot be directly transferred to new environments. We try to transfer the logical knowledge and the generalization ability of pre-trained foundation models to zero-shot navigation.
  arXiv  Detail & Related papers  (2025-02-19T17:27:47Z)
• CoNav: A Benchmark for Human-Centered Collaborative Navigation [66.6268966718022]
  We propose a collaborative navigation (CoNav) benchmark. Our CoNav tackles the critical challenge of constructing a 3D navigation environment with realistic and diverse human activities. We propose an intention-aware agent for reasoning both long-term and short-term human intention.
  arXiv  Detail & Related papers  (2024-06-04T15:44:25Z)
• Embodied Agents for Efficient Exploration and Smart Scene Description [47.82947878753809]
  We tackle a setting for visual navigation in which an autonomous agent needs to explore and map an unseen indoor environment. We propose and evaluate an approach that combines recent advances in visual robotic exploration and image captioning on images. Our approach can generate smart scene descriptions that maximize semantic knowledge of the environment and avoid repetitions.
  arXiv  Detail & Related papers  (2023-01-17T19:28:01Z)
• Towards self-attention based visual navigation in the real world [0.0]
  Vision guided navigation requires processing complex visual information to inform task-orientated decisions. Deep Reinforcement Learning agents trained in simulation often exhibit unsatisfactory results when deployed in the real-world. This is the first demonstration of a self-attention based agent successfully trained in navigating a 3D action space, using less than 4000 parameters.
  arXiv  Detail & Related papers  (2022-09-15T04:51:42Z)
• Rapid Exploration for Open-World Navigation with Latent Goal Models [78.45339342966196]
  We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments. At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images. We use an information bottleneck to regularize the learned policy, giving us (i) a compact visual representation of goals, (ii) improved generalization capabilities, and (iii) a mechanism for sampling feasible goals for exploration.
  arXiv  Detail & Related papers  (2021-04-12T23:14:41Z)
• Occupancy Anticipation for Efficient Exploration and Navigation [97.17517060585875]
  We propose occupancy anticipation, where the agent uses its egocentric RGB-D observations to infer the occupancy state beyond the visible regions. By exploiting context in both the egocentric views and top-down maps our model successfully anticipates a broader map of the environment. Our approach is the winning entry in the 2020 Habitat PointNav Challenge.
  arXiv  Detail & Related papers  (2020-08-21T03:16:51Z)
• Active Visual Information Gathering for Vision-Language Navigation [115.40768457718325]
  Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments. One of the key challenges in VLN is how to conduct a robust navigation by mitigating the uncertainty caused by ambiguous instructions and insufficient observation of the environment. This work draws inspiration from human navigation behavior and endows an agent with an active information gathering ability for a more intelligent VLN policy.
  arXiv  Detail & Related papers  (2020-07-15T23:54:20Z)

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.